Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network

DOI10.1137/20M1323151zbMath1466.37061arXiv2003.01184MaRDI QIDQ4986840

Duane S. Boning, Dylan E. C. Grullon, Kyongmin Yeo, Fan-Keng Sun, Jayant R. Kalagnanam

Publication date: 28 April 2021

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.01184

zbMATH Keywords

recurrent neural networkdynamical systemuncertainty quantificationvariational inferencedeep learningrepresentation learning

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Time series analysis of dynamical systems (37M10) Simulation of dynamical systems (37M05) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (2)

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems ⋮ Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

Uses Software

• EnKF
• Adam
• SGDR
• PRMLT

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