Table of Contents
Overview
Definition and Scope
Computational science, also referred to as scientific computing, is an interdisciplinary field that integrates principles from mathematics, computer science, and the physical sciences to analyze and understand complex systems. This field has significantly transformed the methodologies employed by scientists and researchers in data analysis and simulation, enabling them to tackle intricate real-world problems effectively.[3.1] The scope of computational science encompasses a variety of disciplines, including applied mathematics, data science, engineering, and computing, and it applies to numerous scientific domains such as biology, finance, and more.[4.1] It is characterized by the fusion of numerical methods, particularly in modeling and simulation (M&S), with computer systems—both hardware and software—emphasizing high-performance computing.[2.1] Furthermore, computational science is increasingly interlinked with modern technological fields such as artificial intelligence, machine learning, big data, and deep learning, reflecting its broad applicability and relevance in contemporary research and industry.[4.1]Key Techniques and Methodologies
Computational science employs a variety of key techniques and methodologies that are essential for effective data analysis and modeling. One of the foundational elements is data preprocessing, which plays a critical role in enhancing data quality and model performance. This process involves cleaning, transforming, and organizing raw data into a usable format, thereby improving computational efficiency and making the data ready for analysis, modeling, and prediction.[8.1] Data preprocessing not only saves time and effort for data scientists but also enhances the accuracy of predictions and the effectiveness of decision-making.[7.1] Mathematical modeling is a fundamental technique in computational science, extensively applied across various domains such as natural sciences, engineering, and economics to solve complex problems and analyze real-world phenomena.[13.1] The integration of artificial intelligence (AI) and machine learning has significantly influenced the evolution of mathematical modeling, providing tools that enhance the understanding and optimization of AI systems while also presenting new challenges that necessitate the development of novel mathematical frameworks.[12.1] Furthermore, mathematical modeling has proven instrumental in addressing public health issues, particularly in the context of epidemiology, where interdisciplinary efforts among health scientists, computer scientists, and engineers are crucial for innovative research aimed at improving health behaviors and communication strategies.[15.1] This synergy between mathematical modeling and emerging technologies underscores its vital role in tackling contemporary global challenges. The integration of machine learning methodologies has significantly influenced the field of computational science, particularly in the analysis of data across various scientific domains. Machine Learning for Computational Science and Engineering explores the intricate methodologies and expansive applications of machine learning, highlighting the latest advancements in ML frameworks and their multifaceted uses in science and engineering.[20.1] As artificial intelligence technology continues to evolve, its applications are expected to expand, particularly in public health, where it fosters data-driven approaches to address pressing challenges.[19.1] This evolution underscores the transformative impact of machine learning on data analysis and model development within computational science.In this section:
History
Early Developments
Computational History, sometimes referred to as Histoinformatics, is a multidisciplinary field that studies history through machine learning and other data-driven, computational approaches.[44.1] This field is related to both quantitative history and the broader domain of digital humanities.[45.1] The integration of computational methods into historical research allows for innovative analyses and insights, particularly in projects concerning the history and philosophy of science.[45.1] The early developments in computational history have significantly transformed the study of the history of science. By enabling researchers to pursue novel types of questions and expanding the scale of analysis both geographically and temporally, computational methods have opened new avenues for inquiry.[46.1] Furthermore, these methods have facilitated innovative forms of publication that enhance access and transparency within the field.[46.1] As the discipline evolved, it became evident that computational methods not only influenced the types of questions historians could ask but also highlighted the necessity for improved data management within the history and philosophy of science (HPS) community.[47.1] This shift underscores the growing importance of computational techniques in shaping narratives and methodologies in historical research.Evolution of Computational Techniques
Advancements in computational techniques have significantly transformed historical research methodologies, particularly through the integration of digital tools and machine learning. The emergence of machine learning algorithms, especially deep learning techniques, has enhanced the capabilities of artificial intelligence systems, enabling them to analyze large datasets and identify patterns that may have previously gone unnoticed. This has allowed historians to process vast amounts of historical texts, images, and artifacts, yielding new insights into the past that traditional methods could not achieve.[49.1] Digital tools have revolutionized the teaching and learning of history by contrasting traditional instructional methods with technology-integrated approaches. Traditional methods, which often rely on lectures and textbooks, provide foundational knowledge but lack the engagement offered by digital tools. In contrast, technology-integrated methods utilize online databases, interactive maps, virtual tours, and educational games, making the learning experience more dynamic and accessible.[54.1] The integration of these digital tools has not only enhanced student engagement but has also transformed the research workflow for historians, allowing them to conduct keyword searches across millions of documents and analyze data from the comfort of their offices.[56.1] The integration of digital tools in historical research has significantly transformed traditional methodologies, particularly through the use of digital mapping and geospatial analysis. These tools enable historians to visualize historical data within a spatial context, offering new insights into the unfolding of events and the various factors that influenced historical outcomes.[57.1] The field of digital history has evolved to focus on the study and presentation of historical narratives using digital technologies, enhancing interpretation, collaboration, and accessibility.[58.1] Furthermore, there is a growing trend of interdisciplinary collaboration between historians and computer scientists, where historians formulate research questions and visualize projects, while computer scientists develop innovative tools to process primary sources, thereby broadening the research horizon.[59.1] This collaboration exemplifies the development of hybrid domains such as digital or computational humanities, which bridge traditional boundaries between disciplines and enhance the interaction among computer science, library, and humanities.[61.1] As the scale and complexity of historical records continue to grow, the collaboration between historians and digital humanists is expected to deepen, leading to innovative breakthroughs in computational science and history.[63.1] This partnership is crucial for addressing the challenges posed by the vast amounts of data available, ensuring that historical scholarship remains relevant and impactful in the digital age.In this section:
HistoryDigital HumanitiesHistory And Philosophy Of ScienceHistory Of ScienceData Management
Recent Advancements
Integration with Artificial Intelligence
Recent advancements in computational science have increasingly integrated artificial intelligence (AI) to enhance data analysis and modeling capabilities. One significant development is the emergence of Physics-Informed Neural Networks (PINNs), which combine the accuracy of physics-based modeling with the flexibility of neural networks. Unlike traditional numerical methods, PINNs are mesh-free and can approximate high-dimensional solutions to partial differential equations (PDEs), making them particularly advantageous in complex scenarios where conventional methods may struggle due to simplifying assumptions.[99.1] This integration allows for the simultaneous solving of forward and inverse problems, thereby improving computational efficiency and reducing development effort.[97.1] Furthermore, the role of real-time data analytics has evolved significantly with the advent of AI and the Internet of Things (IoT). AI enhances the management and analysis of vast datasets generated by IoT devices, enabling organizations to derive valuable insights and make real-time data-driven decisions.[103.1] This synergy between AI and IoT facilitates the optimization of operations across various industries, as organizations can respond swiftly to changing conditions.[105.1] For instance, edge AI processes real-time data directly on devices, minimizing reliance on cloud computing and reducing latency, which is crucial for applications such as smart city traffic management.[104.1]High-Performance Computing Innovations
Recent advancements in high-performance computing (HPC) have been significantly influenced by the convergence of various technologies and innovative methodologies. One notable development is the emergence of Physics-Informed Neural Networks (PINNs), which have become a pivotal tool in scientific machine learning since their introduction in 2017. These networks facilitate the efficient resolution of ordinary and partial differential equations by leveraging sparse measurements, thereby enhancing computational efficiency in various scientific applications.[84.1] Additionally, the integration of ubiquitous computing and readily available sensors has transformed data collection and processing capabilities. This integration allows for real-time data acquisition and local processing through powerful embedded systems, which are essential for adaptive and informed decision-making in computational tasks.[85.1] In 2023, significant breakthroughs in algorithms have further propelled the field. Notably, advancements in Shor’s algorithm, which is crucial for quantum computing, have provided a substantial upgrade after nearly three decades, showcasing the potential for solving long-standing computational problems.[87.1] Furthermore, a mathematical framework utilizing random graph theory has been introduced to elucidate the emergent capabilities of large language models (LLMs), such as creativity and compositional generalization. This framework underscores the importance of advanced mathematical tools in driving innovation across artificial intelligence and quantum mechanics.[86.1]In this section:
Applications
Physical and Natural Sciences
Computational science plays a pivotal role in the physical and natural sciences by employing computational methods to model, simulate, visualize, and analyze complex phenomena across various disciplines. This interdisciplinary approach integrates numerical methods, computer systems, and information science, facilitating advancements in fields such as chemistry, physics, genetics, and astronomy.[127.1] In the field of climate science, high-fidelity climate models serve as essential tools for understanding and predicting the impacts of climate change. These models address various computational challenges, including simulation methodologies and data-driven regional climate impact modeling, which are crucial for effective climate change mitigation and adaptation strategies.[140.1] Furthermore, artificial intelligence (AI) and optimized computational modeling are recognized as effective tools that can assist in addressing the challenges posed by the climate crisis. By managing these advancements, we can develop more compelling methods for directing the impacts of climate change and fostering a more sustainable future.[141.1] Additionally, machine learning technologies play a significant role in climate solutions, enhancing the efficiency of electricity systems and smart buildings, while also accelerating climate science research.[142.1] Computational science is a discipline within computer science that enhances knowledge across various scientific fields through computational analysis, modeling, simulation, and numerical approximation. This field integrates numerical methods, particularly emphasizing Modeling & Simulation (M&S), with computer systems and information science, focusing on high-performance computing and data management.[127.1] It plays a crucial role in describing and solving natural systems, as it enables the understanding, processing, and simulation of complex behaviors more efficiently than relying solely on human capability.[127.1] The applications of computational science are extensive and include areas such as astrophysics, genetics, bioengineering, climate and weather prediction, and financial modeling, among others. This breadth of application demonstrates how computational science facilitates advancements across diverse fields through sophisticated computational techniques.[127.1] Recent advancements in quantum computing are ushering in a new era of practical innovation and application, moving beyond theoretical exploration. With improvements in quantum hardware, researchers are poised to achieve groundbreaking scientific breakthroughs that were previously unattainable with classical computers. This progress is particularly evident in fields such as artificial intelligence, machine learning, industrial optimization, and materials simulation, which stand to benefit significantly from the enhanced performance of quantum systems.[132.1] The focus within the quantum computing industry has shifted towards developing larger and more powerful machines, as exemplified by notable achievements in quantum error correction and qubit performance.[132.1] As quantum computing continues to evolve, it promises to reshape the technological landscape and enhance the capabilities of computational science in addressing real-world scientific challenges.[133.1]Social Sciences and Humanities
Computational science has found significant applications in the social sciences and humanities, particularly in modeling and predicting human behavior. One approach involves the use of dynamic models, such as Kalman filters, which are sequenced together by a Markov chain. This methodology allows researchers to recognize and predict human behaviors from sensory data over short time frames, demonstrating the potential of computational models to capture the complexities of human actions.[163.1] However, challenges remain in ensuring the accuracy and generalizability of these models. A critical aspect of the computational model is its reliance on a similarity metric for behaviors, which is defined by a complex mathematical operation involving differences between integers. This raises questions about the extent to which the predictions made by such models can be effectively translated into real human behavior, highlighting the difficulties in representing the nuanced dynamics of human actions accurately.[164.1]Educational Aspects
Curriculum Development in Computational Science
Curriculum development in computational science has evolved significantly over the past two decades, driven by rapid advancements in computational technologies that have influenced various fields, including education.[169.1] A key aspect of this development is the integration of computational thinking (CT) into educational curricula, which is recognized as a vital problem-solving approach that transcends traditional computer science education.[168.1] Research indicates that while programming education alone may not substantially enhance students' problem-solving abilities, incorporating elements of computational thinking into lessons across various subjects—such as mathematics, natural sciences, and language arts—can lead to improvements in students' abstract thinking, troubleshooting, and cooperative learning skills.[170.1] This suggests that a curriculum focused on computational thinking, rather than solely on coding, is more effective in fostering essential cognitive skills among students. The importance of computational thinking has catalyzed the development of educational programs aimed at enhancing its integration into curricula.[171.1] However, the multifaceted nature of computational thinking necessitates clear strategies for its implementation, as educators often face challenges in adapting traditional teaching methods to include these innovative concepts.[172.1] Resistance to change remains a significant barrier, with concerns about increased workloads and inadequate training among faculty and staff hindering the adoption of computational science education.[174.1] To address these challenges, effective strategies for integrating computational thinking into existing curricula have been proposed. For instance, a framework has been developed that includes pedagogical experiences such as unplugged activities, tinkering, making, and remixing, which can help educators introduce computational thinking in a more accessible manner.[179.1] Additionally, collaborative programming and project-based learning are emphasized as methods that not only teach computational skills but also promote essential soft skills like collaboration and communication.[186.1] Furthermore, the curriculum should prioritize key competencies such as problem-solving through computational thinking, a solid mathematical foundation, and foundational programming skills.[187.1] It is essential to recognize that the focus should not solely be on the programming languages taught but rather on the underlying computational thinking skills that enable learners to adapt and transfer their knowledge across various contexts.[187.1]In this section:
Challenges And Future Directions
Current Limitations in Computational Science
Current limitations in computational science are primarily characterized by challenges in multiscale modeling, multi-physics phenomena, and the accurate representation of evolving discontinuities. The concept of multiscale modeling emphasizes that a comprehensive understanding of materials necessitates insights across various time and length scales. This requirement poses significant challenges, particularly in ensuring that descriptions at all levels remain consistent, which is especially demanding for advanced materials and complex fluids.[210.1] One of the critical technical challenges in modeling multiscale phenomena is the need for effective data transfer and synchronization between models operating at different scales and time steps. This complexity can adversely affect the accuracy of simulations, as inconsistent data exchange may lead to erroneous results.[211.1] Additionally, hierarchical modeling has been proposed as a potential solution to these challenges, allowing for different levels of physics to be integrated more effectively.[211.1] Moreover, the implementation of multiscale models at the computational level introduces further difficulties. For instance, coupling various codes designed for single-scale, single-physics simulations into a unified framework remains a significant hurdle.[212.1] This issue highlights the broader challenge of integrating diverse computational approaches to achieve reliable and accurate simulations in materials science.Emerging Trends and Research Opportunities
Emerging trends in computational science are increasingly characterized by the necessity for interdisciplinary collaboration and the integration of advanced technologies. The 2024 SIAM Task Force Report emphasizes that stakeholders in computational science must invest strategically in technology and research, foster interdisciplinary collaborations, and commit to creating an inclusive scientific community to support future advancements.[206.1] This collaborative approach is crucial as breakthroughs in computational science often emerge from the intersection of diverse fields, such as neuroscience and artificial intelligence (AI), where teams comprising experts from various domains can enhance understanding and innovation.[214.1] The role of computational science is expanding into new areas, including simulation-based engineering and decision-making, which exemplify its application in multidisciplinary studies. These applications allow for a comprehensive understanding of complex systems and phenomena.[207.1] Furthermore, the importance of effective organization and management of diverse research teams is highlighted as a prerequisite for addressing complex scientific questions.[217.1] The emergence of "interpreters," individuals who can bridge different scientific disciplines, is also noted as vital for enhancing interdisciplinary collaborations.[215.1] Technological advancements are pivotal in shaping the future of computational science. The International Conference on Technological Advancements in Computational Sciences (ICTACS - 2023) aims to bring together researchers and practitioners to discuss significant topics and generate new ideas.[219.1] Innovations such as quantum computing, AI, and machine learning are transforming industries and creating new applications, thereby altering the landscape of computational science.[222.1] These advancements not only enhance decision-making processes but also redefine communication through technologies like natural language processing and conversational AI.[222.1]In this section:
References
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[3] What is computational science? - California Learning Resource Network — Computational science, also known as scientific computing, is an interdisciplinary field that combines the principles of mathematics, computer science, and the physical sciences to understand and analyze complex systems. It is a rapidly growing field that has revolutionized the way scientists and researchers approach data analysis, simulation
[4] Computational Science - Everything you need to know — In practice, computational science brings together disciplines like applied mathematics, data science, engineering, and computing, along with whatever branch of science the model intends to study- be it biology, finance, or anything else. This also interplays with other modern technological fields of study like Artificial Intelligence, Machine Learning, Big Data, Deep Learning, and so on.
[7] Data Preprocessing -The Foundation of Data Science Solution — Additionally, it makes the data ready for analysis, modeling, and prediction. It also saves time and effort for data scientists, as well as improves the accuracy of predictions and the effectiveness of decision-making. Conclusion Data preprocessing is an essential step in any data science solution.
[8] Data Preprocessing: Definition, Key Steps and Concept — Normalize and scale data for better model performance. Reduce noise and enhance feature engineering. Improve accuracy and efficiency of machine learning algorithms. Information data preprocessing is an essential cycle in information science and AI that includes cleaning, changing, and coordinating crude information into a usable configuration.
[12] [2501.10465] The Mathematics of Artificial Intelligence - arXiv.org — In just 3 minutes help us improve arXiv: arXiv:2501.10465 arXiv identifier arXiv author ID This overview article highlights the critical role of mathematics in artificial intelligence (AI), emphasizing that mathematics provides tools to better understand and enhance AI systems. Conversely, AI raises new problems and drives the development of new mathematics at the intersection of various fields. This article focuses on the application of analytical and probabilistic tools to model neural network architectures and better understand their optimization. Subjects: Optimization and Control (math.OC); Artificial Intelligence (cs.AI) Cite as: arXiv:2501.10465 [math.OC] (or arXiv:2501.10465v1 [math.OC] for this version) From: Gabriel Peyré [view email] cs.AI Bibliographic and Citation Tools Bibliographic Explorer Toggle Connected Papers Toggle scite.ai Toggle Which authors of this paper are endorsers?
[13] Mathematical Modeling - an overview | ScienceDirect Topics — Mathematical Modeling - an overview | ScienceDirect Topics Mathematical Modeling Mathematical Modeling 28.1 Mathematical Modeling Mathematical modeling is widely used in natural sciences, computing, engineering, meteorology, economics and finance. Mathematical modeling. Mathematical Modeling Mathematical Modeling Mathematical Modeling In this article, mathematical modeling concepts in relation to physical sciences, engineering, and technology have been surveyed. Mathematical modelling is one of the basic techniques for solving problems and analyzing reality in Physics and Engineering. Mathematical models This chapter is an introduction to mathematical modelling for engineering systems, later chapters involving more detailed consideration of models. Simulation/Mathematical Model Computational scientists also use mathematical models to describe the physical world (universe) in which we exist. Applied Mathematical Modelling
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[20] Machine Learning for Computational Science and Engineering — Machine Learning for Computational Science and Engineering delves into the intricate realm of machine learning methodologies and their expansive applications across various domains of science and engineering. Focuses on the latest advancements in ML frameworks and their multifaceted applications.
[44] Computational history - Wikipedia — Computational History (not to be confused with computation history), sometimes also called Histoinformatics, is a multidisciplinary field that studies history through machine learning and other data-driven, computational approaches.
[45] The History of Science and the Science of History: Computational ... — We briefly explain how computational history is related to both quantitative history and the digital humanities in general. The second part describes our recent attempts to learn and apply computational methods to a project in the history and philosophy of science using a standardized workflow.
[46] F O C Computational Perspectives in the History of Science — Computational methods and perspectives can transform the history of science by enabling the pursuit of novel types of questions, dramatically expanding the scale of analysis (geographically and temporally), and offering novel forms of publication that greatly enhance access and transparency. This essay presents a brief summary of a computational research system for the history of science
[47] Focus: Computational History and Philosophy of Science — This Focus section examines how computational methods have in-fluenced, and will in uence, the history of science. The essays discuss the new types of fl questions and narratives that computational methods enable and the need for better data management in the history and philosophy of science (HPS) community.
[49] The Impact of Modern Technology on Historical Research — Artificial intelligence and machine learning are revolutionizing historical research by enabling the analysis of large datasets and the identification of patterns that might otherwise go unnoticed. These technologies are being used to analyze historical texts, images, and artifacts, providing new insights into the past.
[54] The Impact of Digital Tools on the Teaching of History: A Comparative ... — Traditional methods, characterized by lectures, textbooks, and worksheets, provide a foundation for historical knowledge but often lack the dynamism and engagement of digital approaches. In contrast, technology-integrated methods leverage digital tools such as online databases, interactive maps, virtual tours, and educational games to enhance
[56] The Transformation of Historical Research in the Digital Age — They garner information from keyword searches, carried out across millions of documents, their research shaped by algorithms they rarely understand. Historians often then visit archives in whirlwind trips marked by thousands of digital photographs, subsequently explored on computer monitors from the comfort of their offices. Almost all aspects of the historian's research workflow have been transformed by digital technology. The Transformation of Historical Research in the Digital Age equips historians to be self-conscious practitioners by making these shifts explicit and exploring their long-term impact. While many of these changes have been individually small, cumulatively they represent a transformation in the way that historical scholarship is researched, written, and published.
[57] The Impact of Digital Technology on Historical Research — Tools and Methods in Digital Historical Research Digital Mapping and Geospatial Analysis. Digital mapping and geospatial analysis tools allow historians to visualize historical data in a spatial context. This can provide new insights into how events unfolded and how different factors influenced historical outcomes.
[58] Digital Tools and Historical Inquiry: a Bibliometric Study of Digital ... — This bibliometric study explores the evolving field of digital history, focusing on its scholarly trends, key contributors, and thematic developments. Digital history involves the study and presentation of historical narratives using digital tools and technologies to enhance interpretation, collaboration, and accessibility. The analysis uses data from the Scopus database, processed with
[59] Roundtable discussion on digital humanities in early China hosted at ... — Participants also discussed the increasing trend of interdisciplinary research collaboration between history and computer science. "The historians pose the research questions and visualize the project," says Cai. "The computer scientists can help provide new tools to process primary sources and expand the research horizon."
[61] Integrated interdisciplinary workflows for research on historical ... — Tasks, interests, and interaction of computer science, library, and humanities when dealing with digital cultural heritage. The development of interdisciplinary research fields such as digital or computational humanities, library, and information studies or digital curation as hybrid domains that bridge interdisciplinary boundaries as well as "traditional barriers between theory and practice
[63] The Future of Research in the Age of AI - College of Letters & Science ... — March 27-28, 2025. Montana State University. Bozeman, Montana. For decades, historians and digital humanists have been at the forefront of collaborative, computational and digital research, but the scale, volume and complexity of historical records have expanded far beyond the capacity of individual scholars to organize.
[84] From PINNs to PIKANs: recent advances in physics-informed machine ... — Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Scientific machine learning through physics-informed neural networks: Where we are and what’s next. Zou Z, Meng T, Chen P, Darbon J, Karniadakis GE. Estimates on the generalization error of physics-informed neural networks for approximating PDEs. IMA J Numer Anal. Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs. IMA J Numer Anal. SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks.
[85] 25 Latest Technologies in Computer Science in 2025 - Intellipaat — The field’s development stems from the convergence of technologies such as ubiquitous computing, which integrates computing capabilities into everyday objects; readily available sensors for real-time data collection; powerful embedded systems enabling local data processing; and the integration of machine learning for adaptive and informed decision-making. Data Science Big Data Analytics Courses Business Intelligence Courses Salesforce Courses Cloud Computing Courses Digital Marketing Courses AI & Machine Learning Courses Programming Courses Database Courses Project Management Courses Cyber Security and Ethical Hacking Courses Web Development Courses Software Testing Courses Automation Courses Job Oriented Courses Degree Courses Cloud Computing Data Science Machine Learning What is AWS Digital Marketing Cyber Security Salesforce Artificial Intelligence
[86] 2024's Biggest Breakthroughs in Computer Science - Geeky Gadgets — At the heart of these discoveries are two new achievements: a mathematical framework that explains how large language models (LLMs) like GPT-4 achieve their surprising creativity and an efficient algorithm that unlocks new ways to understand quantum systems at low temperatures. Researchers introduced a mathematical framework using random graph theory to explain emergent capabilities in large language models (LLMs), such as creativity and compositional generalization. Both breakthroughs underscore the importance of advanced mathematical tools in driving innovation across AI and quantum mechanics, paving the way for fantastic interdisciplinary applications. Similarly, the Hamiltonian learning algorithm offers a robust framework for addressing complex challenges in quantum mechanics, with potential applications in material science, quantum hardware development, and energy research. Filed Under: AI, Technology News, Top NewsLatest Geeky Gadgets Deals
[87] The Biggest Discoveries in Computer Science in 2023 - Quanta Magazine — Comments Read Later Read Later Previous: 2023 in Review The Year in Biology Next: 2023 in Review The Year in Physics SERIES 2023 in Review The Year in Computer Science By Bill Andrews December 20, 2023 Artificial intelligence learned how to generate text and art better than ever before, while computer scientists developed algorithms that solved long-standing problems. Video: In 2023, computer scientists made progress on a new vector-driven approach to AI, fundamentally improved Shor’s algorithm for factoring large numbers, and examined the surprising and powerful behaviors that can emerge from large language models. Large language models such as those behind ChatGPT fueled a lot of this excitement, even as researchers still struggled to pry open the “black box” that describes their inner workings. Shor’s algorithm, the long-promised killer app of quantum computing, got its first significant upgrade after nearly 30 years.
[97] Physics-informed neural networks: A deep learning framework for solving ... — Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations - ScienceDirect Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. We have introduced physics-informed neural networks, a new class of universal function approximators that is capable of encoding any underlying physical laws that govern a given data-set, and can be described by partial differential equations. In this work, we design data-driven algorithms for inferring solutions to general nonlinear partial differential equations, and constructing computationally efficient physics-informed surrogate models.
[99] Physics-Informed Neural Networks and Classical Numerical Methods — Physics-Informed Neural Networks (PINNs) represent a groundbreaking approach that merges the accuracy of physics-based modeling with the flexibility of neural networks. Traditional physics-based models often struggle with complex real-world scenarios due to their reliance on simplifying assumptions.
[103] IoT and Artificial Intelligence: Combining Data Analytics and Machine ... — The Internet of Things (IoT) and artificial intelligence (AI) are two paradigm-shifting technologies that have the potential to upend many industries and improve people's lives. They can develop solid solutions using AI systems' data analytics and machine learning capabilities. This fusion of technologies enables organizations to make real-time data-driven decisions, optimize operations
[104] Edge AI for Real-Time Analytics on IoT Devices - Elinext — I deploy the model directly to the edge device, where it runs inference on real-time data. Smart Cities: Edge AI helps optimize traffic management by processing data from cameras and sensors in real time. To successfully implement Edge AI for real-time analytics on IoT devices, let’s use tools like TensorFlow Lite, Edge Impulse, and preprocessing libraries to help with real-time data analytics at the edge. After preprocessing the data and converting the model to TensorFlow Lite, the next step is running inference on the edge device. This code shows how to load a TensorFlow Lite model on the edge and run real-time inference using preprocessed input data. However, implementing Edge AI requires careful consideration of hardware, model optimization, and data preprocessing.
[105] From Real-Time Insights to AI: The Data Revolution of 2025 — Darwoft - From Real-Time Insights to AI: The Data Revolution of 2025 From Real-Time Insights to AI: The Data Revolution of 2025 Data is reshaping industries in 2025 with trends like AI-powered analytics, real-time insights, and robust privacy measures. Trends like real-time data processing, privacy and compliance tools, and AI-powered analytics are shaping the future. Tools like customer data platforms (CDPs) bring all customer information together, helping businesses predict what customers need and improve their services. New techniques like federated learning allow AI models to learn from data without sharing it, keeping sensitive information safe and meeting HIPAA requirements. Whether it’s using real-time analytics, improving data readiness, or applying AI tools, data is shaping the future of every industry.
[127] Computational Science - an overview | ScienceDirect Topics — Computational science refers to the discipline within computer science that advances knowledge in various scientific fields through the use of computational analysis, modeling, simulation, and numerical approximation. Computational science is often described as fusing numerical methods (with a special focus on Modeling & Simulation (M&S)), computer systems (software and hardware, with a special focus on High Performance Computing) and information science (with a special focus on data management). Astrophysics Fracture Analysis Atomic and Molecular Physics Genetics Bioengineering and Biophysics Groundwater and Contaminant Flow Chemistry Inertial Confinement Fusion Climate and Weather Prediction Magnetic Fusion Energy Computational Biology Materials Science Computational Fluid Dynamics Medicine Cosmology Nanotechnology and Nanoscience Cryptography Optics Data Mining Scientific Databases Earth Systems Shock Hydrodynamics Earthquakes and Volcanoes Space weather Engineering Design and Analysis Weather Prediction Finance Wild Fire analysis
[132] Quantum Capabilities Forecast to Break Barriers in 2025 — Quantum With advancements in quantum hardware resulting in new levels of system performance, the world's leading researchers will increasingly embrace quantum technology to achieve groundbreaking scientific breakthroughs and new discoveries out of the reach of classical computers. Areas like AI/ML, industrial optimization and materials simulation stand to benefit greatly from the continued product development progress and increasingly powerful performance of quantum systems. “From Google’s achievements in quantum error correction to Oxford Ionics’ world records in qubit performance, 2024 was a year marked by incredible milestones for the quantum computing industry. Where quantum computing companies once battled it out over research & development, the focus has shifted to making bigger and better machines – not just trying to get the basics working well enough.
[133] The latest developments in quantum computing: A transformative frontier — The latest developments in quantum computing: A transformative frontier | Open Access Government Home Open Access News Research & Innovation The latest developments in quantum computing: A transformative frontier The latest developments in quantum computing: A transformative frontier Quantum computing is no longer confined to theoretical exploration; it is entering a phase of practical innovation and application. With advancements in hardware, algorithms, and integration with other technologies, quantum computing promises to reshape the technological landscape. Quantum Computing Open Access Government produces compelling and informative news, publications, eBooks, and academic research articles for the public and private sector looking at health, diseases & conditions, workplace, research & innovation, digital transformation, government policy, environment, agriculture, energy, transport and more.
[140] Climate Change Modeling: Computational Opportunities and Challenges ... — High-fidelity climate models are the workhorses of modern climate change sciences. In this article, the authors focus on several computational issues associated with climate change modeling, covering simulation methodologies, temporal and spatial modeling restrictions, the role of high-end computing, as well as the importance of data-driven regional climate impact modeling.
[141] Artificial intelligence and optimized computational modelling against ... — By and large, AI and optimized computational modeling are successful devices that can offer assistance to us and address the challenges of the climate crisis. By handling the control of these progresses, we will develop more compelling methods for directing the impacts of climate change and building a more viable future.
[142] Climate change and machine learning — the good, bad, and unknown — Climate change and machine learning — the good, bad, and unknown – MIT EECS Organizations need to continuously push the boundaries of diverse machine learning technologies to meet climate change challenges while considering their energy costs, according to MIT professor Priya Donti. Machine learning can help with climate solutions across several fronts, including improving the efficiency of electricity systems and smart buildings and accelerating climate science research. Machine learning has a significant impact on energy and water resources, given the computational horsepower required for processing and training large models and the water needed to cool data centers, Donti said. Though it isn’t known how much AI contributes to those emissions, energy use related to developing, training, and running machine learning models is undoubtedly rising, Donti said.
[163] Modeling and Prediction of Human Behavior - IEEE Xplore — We propose that many human behaviors can be accurately described as a set of dynamic models (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to
[164] Empirically validating a computational model of automatic behavior ... — Furthermore, the computational model relies on a similarity metric for behaviors that is defined by a difficult-to-translate mathematical operation (i.e. differences between integers). Therefore, the degree to which the predictions made by the computational shaping model generalize to human behavior is unknown.
[168] The Overview of Computer Science/Computational Thinking Education Research — Computer Science Education vs. Computational Thinking While computer science is an individual academic discipline, computational thinking is a problem-solving approach that integrates across
[169] Education in Computational Sciences - ScienceDirect — Keywords: Computational education, key competences, study programmes 1 Introduction The last two decades have witnessed an enormously rapid development of computational technologies which have undoubtedly affected all the fields of human activities, including education. In fact, computational science is one of the most evolving profile study
[170] PDF — One widely-held belief is that computational thinking, and as a result coding and programming education, has a positive effect on students’ problem-solving abilities. Results from these studies show conflicting opinions in computational thinking literature when it comes to the question of whether programming education on its own has a meaningful effect in the problem-solving abilities of students. But studies where components of computational thinking are employed show an increase in the students’ problem-solving, abstract-thinking, troubleshooting and cooperative learning abilities. Upon analysis of these studies, it becomes apparent that it is lessons in coding, mathematics, natural sciences, social sciences and language arts, taught according to computational thinking skills and not mere programming or coding education, which affect an increase in the problem-solving, abstract thinking, troubleshooting, procedural thinking and similar abilities in students.
[171] On Computational Thinking and STEM Education | Journal for STEM ... — The recognized importance of computational thinking has helped to propel the rapid development of related educational efforts and programs over the past decade. Given the multi-faceted nature of computational thinking, which goes beyond programming and computer science, however, approaches and practices for developing students' computational thinking are not always self-explanatory in terms
[172] PDF — stakeholders, as computational thinking were relatively novel concepts requiring awareness-building efforts to underscore their significance. Overcoming resistance to change and traditional teaching methods required providing educators with sufficient training, resources, and support for integrating computational thinking into the curriculum.
[174] Overcoming Technology in Higher Education Bias - raftr — However, resistance to change remains a significant barrier for many institutions, slowing down the adoption of these innovative tools. Faculty and staff—the very people responsible for implementing these technologies—often express concerns about increased workloads, inadequate training, and fears of job displacement.
[179] A Pedagogical Framework for Computational Thinking — Our goal in this paper is to propose a Computational Thinking Pedagogical Framework (CTPF), developed from constructionism and social-constructivism theories. CTPF includes four pedagogical experiences: (1) unplugged, (2) tinkering, (3) making, and (4) remixing. Unplugged experiences focus on activities implemented without the use of computers. Tinkering experiences primarily involve
[186] Teaching Students to Think Like Programmers | Learning.com — Teaching computational thinking also promotes essential soft skills such as collaboration, communication and resilience. Collaborative programming and project-based learning encourage students to work together, share ideas and give and receive constructive feedback, building teamwork and interpersonal skills.
[187] PDF — The findings indicated that: (1) the core competencies for K-12 CS education include problem-solving through computational thinking, math background, and foundational programming skills, and (2) what is essential is not the programming languages taught in K-12 schools but computational thinking, which enables the learners to easily transfer
[206] SIAM Task Force Anticipates Future Directions of Computational Science — To support the ultimate vision of the 2024 SIAM Task Force Report on the Future of Computational Science, all computational science stakeholders will need to make strategic investments in technology and research, foster interdisciplinary collaborations, and commit to an inclusive and innovative scientific community.
[207] Computational science for a better future - ScienceDirect — Third, computational science supports the development of new areas and directions in science, for example, simulation-based engineering science or simulation-based decision-making .Also, one of the important application areas of computational science is multidisciplinary studies, where a combination of models may provide a comprehensive view of the nature of systems and phenomena.
[210] Multiscale Modeling in Advanced Materials Research: Challenges, Novel ... — The concept of multiscale modeling embodies the idea that a comprehensive description of a material will require an understanding over multiple time and length scales. A multiscale model requires that descriptions at all levels be consistent with each other, which can be particularly demanding for advanced materials and complex fluids. For crystalline materials, emerging modeling approaches
[211] Multi-Scale CFD Simulations: Challenges and ... - DimensionLab — Data Transfer and Synchronization: Ensuring consistent data exchange between models operating at different scales and time steps is a complex task that can impact the simulation's accuracy. Addressing the Challenges. 1. Hierarchical Modeling. One approach to overcoming these challenges is hierarchical modeling, where different levels of physics
[212] Multiscale modelling: approaches and challenges - PMC — In addition to the physical and mathematical complexity at the conceptual level, another issue present in many domains is how to implement multiscale models in practice at the computational level. For example, there is the issue of coupling different codes written for single-scale single-physics simulation in a unified framework.
[214] The Importance and Future of Interdisciplinary Collaboration in Science — Interdisciplinary collaboration in scientific research is not just a buzzword; it’s becoming the lifeblood of innovation, especially in fields like neuroscience and artificial intelligence (AI). By fostering interdisciplinary collaboration and addressing the ethical implications of AI, we can ensure that the future of technology aligns with our understanding of human cognition, ultimately enhancing both fields and their impact on society. For example, the collaborative efforts of Fernandez-Ruiz’s team at Cornell University, which includes neuroscientists and AI experts, highlight how diverse expertise can converge to enhance our understanding of cognition and behavior. Posted in Colleges News by LLMBy Forward PathwayPosted on January 26, 2025Tagged Artificial Intelligence, Cornell University, Duke University, Innovation, Interdisciplinary Collaboration, neuroscience, Scientific Research, teamwork, University of Southern Mississippi
[215] Full article: The Future of Interdisciplinary Research in the Digital ... — Our findings derived from one-on-one interviews (n = 22) reinforce the importance of interdisciplinary collaboration and highlight the significance of "interpreters," i.e., individuals able to communicate with and connect various areas of science, education, and academic institutions' role in enhancing interdisciplinary collaborations of
[217] The Rewards and Challenges of Interdisciplinary Collaborations - Cell Press — Insight from, and dialog between, multiple scientific disciplines is accepted as a necessity when trying to understand complex scientific questions. An important prerequisite of finding answers to these queries, however, is the ability to effectively organize and manage diverse research teams. Zhiyong Jason Ren (@zjasonren), an environmental engineer and interdisciplinary project lead guru
[219] ICTACS - 2023 - Amity University — The International Conference on Technological Advancements in Computational Sciences (ICTACS - 2023), is being organized with the prime objective to bring together research scholars, practicing scientists, and industrialists from across the world, and also from wide spectrum of engineering and management domains, onto a common platform for
[222] Top 10 Emerging Trends In Computer Science Engineering - BNMIT — Top 10 Emerging Trends In Computer Science Engineering | BNMIT The impact of data science and big data analytics in transforming industries and enhancing decision-making processes solidifies their standing as core trends in Computer Science Engineering. The proliferation of these applications solidifies NLP and Conversational AI as pivotal trends in Computer Science Engineering for 2023. From the transformative potential of Artificial Intelligence and Machine Learning to the game-changing impact of Edge Computing, the rise of blockchain technology, and the promise of Quantum Computing, these trends shape the future of our digital world. Data Science and Big Data Analytics enable data-driven decision-making, and Natural Language Processing and Conversational AI systems are redefining communication.