A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335)

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English	A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations	scientific article

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title

A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (English)

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Journal of Computational Physics

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publication date

3 June 2022

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zbMATH Keywords

physics informed neural network (PINN)

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auxiliary physics informed neural network (A-PINN)

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integro-differential equations (IDEs)

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deep learning

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multi-output neural network

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2022.111260

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cites work

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

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Q4558473

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Physics-informed neural networks for high-speed flows

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Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

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Non-invasive inference of thrombus material properties with physics-informed neural networks

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Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions

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Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions

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

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PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

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Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

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fPINNs: Fractional Physics-Informed Neural Networks

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

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Meta-learning PINN loss functions

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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When and why PINNs fail to train: a neural tangent kernel perspective

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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Learning functional priors and posteriors from data and physics

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

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Multi-fidelity Bayesian neural networks: algorithms and applications

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Adversarial uncertainty quantification in physics-informed neural networks

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Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

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Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations

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Consumer memory and price fluctuations in community markets: An integrodifferential model

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Integro-differential equations for option prices in exponential Lévy models

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A sixth-order compact finite difference method for non-classical vibration analysis of nanobeams including surface stress effects

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Travelling waves for integro-differential equations in population dynamics

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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A new algorithm for the solution of nonlinear two-dimensional Volterra integro-differential equations of high-order

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2022.111260

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Mathematics(1 entry)

mardi Publication:2671335