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Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next.

, , , , , and . J. Sci. Comput., 92 (3): 88 (2022)

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Machine Learning of Linear Differential Equations using Gaussian Processes., and . CoRR, (2017)Machine Learning of Space-Fractional Differential Equations., , , and . CoRR, (2018)Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations.. J. Mach. Learn. Res., (2018)A deep learning framework for solution and discovery in solid mechanics: linear elasticity., , , , and . CoRR, (2020)Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations, , and . (2017)cite arxiv:1711.10561.Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations, , and . (2017)cite arxiv:1711.10566.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)Deep Learning of Turbulent Scalar Mixing., , and . CoRR, (2018)Hidden physics models: Machine learning of nonlinear partial differential equations., and . J. Comput. Phys., (2018)