A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

•The present method can learn both linear and nonlinear correlations between the low- and high-fidelity data adaptively.•The present method can infer the quantities of interest based on a few scattered data.•The present method can identify the unknown parameters in the PDEs.•The present method can b...

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Vydané v:	Journal of computational physics Ročník 401; s. 109020
Hlavní autori:	Meng, Xuhui, Karniadakis, George Em
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge Elsevier Inc 15.01.2020 Elsevier Science Ltd
Predmet:	Accuracy Adversarial data Approximation Benchmarks Computational physics Correlation Gaussian process Multi-fidelity Neural networks Parameter identification Partial differential equations Physics-informed neural networks Porous media Reactive transport Training Unsaturated flow Porous media Reactive transport Multi-fidelity Physics-informed neural networks Adversarial data
ISSN:	0021-9991, 1090-2716
On-line prístup:	Získať plný text
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