Stochastic embeddings of dynamical phenomena through variational autoencoders

DOI10.1016/j.jcp.2022.110970OpenAlexW3092768252MaRDI QIDQ2133707

Jesús M. Presedo, Constantino A. García, Abraham Otero, Paulo Félix

Publication date: 5 May 2022

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

Full work available at URL: https://arxiv.org/abs/2010.06265

zbMATH Keywords

stochastic differential equationGaussian process state space modelstructured variational autoencoder

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Nonparametric inference (62Gxx) Probabilistic methods, stochastic differential equations (65Cxx)

Uses Software

• PMTK
• TensorFlow
• GPflow
• PILCO
• torchdiffeq
• torchsde

Cites Work

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• A data-driven approximation of the koopman operator: extending dynamic mode decomposition
• A survey of numerical methods for stochastic differential equations
• Embedology
• Practical method for determining the minimum embedding dimension of a scalar time series
• Hidden physics models: machine learning of nonlinear partial differential equations
• Dynamic mode decomposition of numerical and experimental data
• Probability for Statistics and Machine Learning
• Particle Markov Chain Monte Carlo Methods
• Nonlinear Time Series Analysis
• Learning latent dynamics for partially observed chaotic systems
• Robust Filtering and Smoothing with Gaussian Processes
• A Stochastic Approximation Method

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