Physics-informed Machine Learning Publications

Publications in Physics-informed Machine Learning

Explore the most influential research papers and scholarly works that have shaped Physics-informed Machine Learning. Use the concept filter below to focus on a specific sub-area of research.

Physics-informed Machine Learning

322

Publications

42.3K

Citations

Showing yearly statistics from 2000-2026.

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
Machine learning and the physical sciences Giuseppe Carleo, K. Cranmer, Laurent Daudet, Reviews of Modern Physics Artificial IntelligenceComputational ScienceEngineeringMachine LearningData Science	2019	2.3K
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
Hidden physics models: Machine learning of nonlinear partial differential equations Maziar Raissi, George Em Karniadakis Journal of Computational Physics Numerical AnalysisEngineeringMachine LearningPde-constrained OptimizationNonlinear Process	2017	1.3K
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics Ehsan Haghighat, Maziar Raissi, Adrian Moure, Computer Methods in Applied Mechanics and Engineering EngineeringMachine LearningPhysicsSurrogate ModelingPhysic Aware Machine Learning	2021	1K
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
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis Journal of Computational Physics Evolving Neural NetworkEngineeringMachine LearningPhysicsDeep Reinforcement Learning	2019	911
Data-driven science and engineering: machine learning, dynamical systems, and control Thomas Peters Contemporary Physics Artificial IntelligenceData-driven PlanningData-driven OptimizationData-driven ScienceEngineering	2019	622
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chenxi Wu, Min Zhu, Qinyang Tan, Computer Methods in Applied Mechanics and Engineering Comprehensive StudyEngineeringMachine LearningPhysicsPhysic Aware Machine Learning	2022	504
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework Physical Review Fluids Physics-informed MachineEngineeringMachine LearningMachine Learning ToolTurbulence	2018	473
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Liu Yang, Dongkun Zhang, George Em Karniadakis SIAM Journal on Scientific Computing EngineeringMachine LearningNeural NetworkGenerative SystemStochastic Differential Equations	2020	396
Adversarial uncertainty quantification in physics-informed neural networks Yibo Yang, Paris Perdikaris Journal of Computational Physics Physic Aware Machine LearningUncertainty QuantificationUncertainty ModelingUncertainty PrinciplePhysics-informed Machine Learning	2019	394

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