wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777)

scientific article; zbMATH DE number 7821074

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English	wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws	scientific article; zbMATH DE number 7821074

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instance of

scholarly article

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title

wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (English)

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SIAM Journal on Numerical Analysis

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publication date

20 March 2024

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full work available at URL

https://arxiv.org/abs/2207.08483

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zbMATH Keywords

PINNs

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hyperbolic conservation laws

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deep learning

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MaRDI publication profile

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cites work

Total Variation Minimization with Finite Elements: Convergence and Iterative Solution

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On the approximation of functions by tanh neural networks

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Neural‐network‐based approximations for solving partial differential equations

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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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Front tracking for hyperbolic conservation laws

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When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

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

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NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

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A theoretical analysis of deep neural networks and parametric PDEs

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Finite Volume Methods for Hyperbolic Problems

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Deep learning observables in computational fluid dynamics

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Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks

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Physics-informed neural networks for high-speed flows

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Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

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Estimates on the generalization error of physics-informed neural networks for approximating PDEs

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Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences

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fPINNs: Fractional Physics-Informed Neural Networks

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Thermodynamically consistent physics-informed neural networks for hyperbolic systems

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Optimal approximation of piecewise smooth functions using deep ReLU neural networks

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Hidden physics models: machine learning of nonlinear partial 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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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ

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Parallel physics-informed neural networks via domain decomposition

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

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Mathematics(1 entry)

mardi Publication:6197777