Learning data-driven discretizations for partial differential equations

The numerical solution of partial differential equations (PDEs) is challenging because of the need to resolve spatiotemporal features over wide length- and timescales. Often, it is computationally intractable to resolve the finest features in the solution. The only recourse is to use approximate coa...

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Veröffentlicht in:	Proceedings of the National Academy of Sciences - PNAS 2019-07, Vol.116 (31), p.15344-15349
Hauptverfasser:	Bar-Sinai, Yohai, Hoyer, Stephan, Hickey, Jason, Brenner, Michael P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Differential equations Discretization Finite difference method Learning Mathematical models Neural networks Nonlinear equations Numerical methods Partial differential equations Physical Sciences
Online-Zugang:	Volltext
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