%0 Generic %1 raissi2017physics %A Raissi, Maziar %A Perdikaris, Paris %A Karniadakis, George Em %D 2017 %K deeplearning neuralode neuralpde physics %T Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations %U http://arxiv.org/abs/1711.10566 %X 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 algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.

@misc{raissi2017physics, abstract = {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 algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.}, added-at = {2022-03-24T16:17:51.000+0100}, author = {Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em}, biburl = {https://www.bibsonomy.org/bibtex/211d6eef138a0c2417c7016ec497c9d51/annakrause}, description = {Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations}, interhash = {63fb6157f55cf5e4800b42768daf335f}, intrahash = {11d6eef138a0c2417c7016ec497c9d51}, keywords = {deeplearning neuralode neuralpde physics}, note = {cite arxiv:1711.10566}, timestamp = {2022-10-12T14:52:59.000+0200}, title = {Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations}, url = {http://arxiv.org/abs/1711.10566}, year = 2017 }