Paris Perdikaris - Concepedia

Author

Paris Perdikaris

Also Known As

P. Perdikaris, Paris G. Perdikaris, Paris Perdikaris, Perdikaris, Paris, Scott Damrauer, Stinis, Panos, Turner, Richard E.

158

Publications

34.1K

Citations

H-Index

138

Concepts

All Affiliations

University of Pennsylvania Brown University Massachusetts Institute of Technology Applied Mathematics (United States)Pontificia Universidad Católica de Chile

About

Paris Perdikaris is an author at University of Pennsylvania specializing in engineering, machine learning, and data science.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	41	57	25.9K
2	Public Health	5	5	479
3	Natural Sciences	4	4	2.5K
4	Management	2	2	418
5	Medicine	1	1	95

Paris Perdikaris

Publications

27.6K

Citations

H-Index

Page 1

	Year	Citations
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Maziar Raissi, Paris Perdikaris, George Em Karniadakis Journal of Computational Physics EngineeringPde-constrained OptimizationDeep Learning FrameworkAi FoundationInverse Problems	2018	14.4K
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks Sifan Wang, Yujun Teng, Paris Perdikaris SIAM Journal on Scientific Computing Numerical AnalysisActive SubscriptionEngineeringMachine LearningPhysics-informed Neural Networks	2021	1.3K
Physics-Informed Neural Networks for Heat Transfer Problems Shengze Cai, Zhicheng Wang, Sifan Wang, Journal of Heat Transfer Heat Transfer ProcessEngineeringMachine LearningDiscretization ErrorsPhysic Aware Machine Learning	2021	1K
When and why PINNs fail to train: A neural tangent kernel perspective Sifan Wang, Xinling Yu, Paris Perdikaris Journal of Computational Physics Cognitive ScienceEngineeringMachine LearningNeural MechanismPinns Fail	2021	991
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data Yinhao Zhu, Nicholas Zabaras, Phaedon‐Stelios Koutsourelakis, Journal of Computational Physics EngineeringMachine LearningData SciencePhysicsUncertainty Quantification	2019	985
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations Maziar Raissi, Paris Perdikaris, George Em Karniadakis arXiv (Cornell University) Artificial IntelligenceGeometric LearningEngineeringMachine LearningAi Foundation	2017	961
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets Sifan Wang, Hanwen Wang, Paris Perdikaris Science Advances EngineeringMachine LearningMachine Learning ToolRecurrent Neural NetworkParametric Pdes	2021	618
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Georgios Kissas, Yibo Yang, Eileen Hwuang, Computer Methods in Applied Mechanics and Engineering EngineeringMachine LearningData SciencePhysic Aware Machine LearningCardiovascular Flows	2019	581
Machine learning of linear differential equations using Gaussian processes Maziar Raissi, Paris Perdikaris, George Em Karniadakis Journal of Computational Physics Gaussian ProcessMachine Learning	2017	571
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang, Hanwen Wang, Paris Perdikaris Computer Methods in Applied Mechanics and Engineering Multi-scale PdesMachine LearningPhysicsEngineeringNatural Sciences	2021	549

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