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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

, , and . J. Comput. Phys., (2019)

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Deep Learning of Turbulent Scalar Mixing., , and . CoRR, (2018)Physics-Informed Neural Networks and Extensions., , , and . CoRR, (2024)Machine Learning of Linear Differential Equations using Gaussian Processes., and . CoRR, (2017)A deep learning framework for solution and discovery in solid mechanics: linear elasticity., , , , and . CoRR, (2020)Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations, , and . (2017)cite arxiv:1711.10566.Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations, , and . (2017)cite arxiv:1711.10561.Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022., , , and . Big Data, 9 (6): 409-410 (2021)Temporal Consistency Loss for Physics-Informed Neural Networks., , , and . CoRR, (2023)Mixing Natural and Synthetic Images for Robust Self-Supervised Representations., , , and . CoRR, (2024)Guest Editorial: Special Issue on Physics-Informed Machine Learning., , , , , and . IEEE Trans. Artif. Intell., 5 (3): 964-966 (March 2024)