PI-VAE: physics-informed variational auto-encoder for stochastic differential equations

DOI10.1016/j.cma.2022.115664OpenAlexW4307296250WikidataQ115358826 ScholiaQ115358826MaRDI QIDQ2679439

Hadi Meidani, Weiheng Zhong

Publication date: 20 January 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.11363

zbMATH Keywords

stochastic differential equationsvariational autoencodersphysics-informed deep learning

Mathematics Subject Classification ID

Numerical analysis (65-XX) Quantum theory (81-XX)

Related Items (6)

Fully probabilistic deep models for forward and inverse problems in parametric PDEs ⋮ PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations ⋮ Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations ⋮ Physics-informed variational inference for uncertainty quantification of stochastic differential equations ⋮ Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions ⋮ A new numerical approach method to solve the Lotka-Volterra predator-prey models with discrete delays

Uses Software

• PyTorch
• Adam
• Wasserstein GAN

Cites Work

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• Neural‐network‐based approximations for solving partial differential equations
• Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance
• Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
• Iterative Bregman Projections for Regularized Transportation Problems
• Data-Driven Identification of Parametric Partial Differential Equations

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