Hidden physics models: Machine learning of nonlinear partial differential equations

While there is currently a lot of enthusiasm about “big data”, useful data is usually “small” and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-e...

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Veröffentlicht in:	Journal of computational physics Jg. 357; S. 125 - 141
Hauptverfasser:	Raissi, Maziar, Karniadakis, George Em
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 15.03.2018 Elsevier Science Ltd
Schlagworte:	Applications of mathematics Artificial intelligence Bayesian modeling Complexity Computational fluid dynamics Computational physics Data management Fractional equations Gaussian process Machine learning Mathematical models Navier-Stokes equations Nonlinear differential equations Nonlinear equations Normal distribution Partial differential equations Physics Probabilistic inference Probabilistic machine learning Small data System identification Time dependence Uncertainty quantification Small data Uncertainty quantification Fractional equations System identification Bayesian modeling Probabilistic machine learning
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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