GINNs: graph-informed neural networks for multiscale physics

DOI10.1016/j.jcp.2021.110192OpenAlexW3037717442MaRDI QIDQ2120776

Søren Taverniers, Markos A. Katsoulakis, Eric J. Hall, Daniel M. Tartakovsky

Publication date: 1 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110192

zbMATH Keywords

Bayesian networksurrogate modeluncertainty propagationdeep learningelectrical double layer capacitor (EDLC)probabilistic graphical model (PGM)

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx) Flows in porous media; filtration; seepage (76Sxx)

Related Items (10)

Mutual information for explainable deep learning of multiscale systems ⋮ Model Uncertainty and Correctability for Directed Graphical Models ⋮ Learning finite element convergence with the multi-fidelity graph neural network ⋮ Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) ⋮ A deep learning energy method for hyperelasticity and viscoelasticity ⋮ Model discovery of compartmental models with graph-supported neural networks ⋮ Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons ⋮ The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation ⋮ Data-driven Whitney forms for structure-preserving control volume analysis ⋮ Uncertainty Quantification for Markov Random Fields

Uses Software

• TensorFlow
• PyTorch
• DGM

Cites Work

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