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Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
[article]
2017
arXiv
pre-print
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of partial differential equations. Depending on whether the available data is scattered in space-time or arranged in fixed temporal snapshots, we introduce two main classes of
arXiv:1711.10566v1
fatcat:tuutvwlaejbp3ejcfxmsr76lkq