PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations (Q6151340)

scientific article; zbMATH DE number 7814771

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English	PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations	scientific article; zbMATH DE number 7814771

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instance of

scholarly article

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title

PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations (English)

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arXiv ID

2307.11289

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DOI

10.4208/nmtma.oa-2023-0044

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Numerical Mathematics: Theory, Methods and Applications

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publication date

11 March 2024

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full work available at URL

https://arxiv.org/abs/2307.11289

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Mathematics Subject Classification ID

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stochastic differential equations

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physics-informed

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variational inference

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generative adversarial networks

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inverse problems

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MaRDI profile type

MaRDI publication profile

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OpenAlex ID

W4386471872

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cites work

Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations

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Q4558473

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Numerical solutions of stochastic differential equations -- implementation and stability issues

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Quasi-Monte Carlo Sampling for Solving Partial Differential Equations by Deep Neural Networks

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Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

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Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

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Failure-Informed Adaptive Sampling for PINNs

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Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models

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Physics-informed neural networks for inverse problems in supersonic flows

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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A survey of numerical methods for stochastic differential equations

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations

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Runge-Kutta methods for numerical solution of stochastic differential equations

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A New Artificial Neural Network Method for Solving Schrödinger Equations on Unbounded Domains

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Sparse Deep Neural Network for Nonlinear Partial Differential Equations

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A Local Deep Learning Method for Solving High Order Partial Differential Equations

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Adversarial uncertainty quantification in physics-informed neural networks

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

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Sitelinks

Mathematics(1 entry)

mardi Publication:6151340