Data-driven discovery of coarse-grained equations
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Publication:2124010
DOI10.1016/j.jcp.2021.110219OpenAlexW3003632398MaRDI QIDQ2124010
Joseph Bakarji, Daniel M. Tartakovsky
Publication date: 14 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.00790
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Parabolic equations and parabolic systems (35Kxx)
Related Items (6)
Extended dynamic mode decomposition for inhomogeneous problems ⋮ Explicit physics-informed neural networks for nonlinear closure: the case of transport in tissues ⋮ Information geometry of physics-informed statistical manifolds and its use in data assimilation ⋮ Machine learning of nonlocal micro-structural defect evolutions in crystalline materials ⋮ Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks ⋮ Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models
Uses Software
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