CEENs: causality-enforced evolutional networks for solving time-dependent partial differential equations (Q6557798)

scientific article; zbMATH DE number 7867427

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English	CEENs: causality-enforced evolutional networks for solving time-dependent partial differential equations	scientific article; zbMATH DE number 7867427

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instance of

scholarly article

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title

CEENs: causality-enforced evolutional networks for solving time-dependent partial differential equations (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

18 June 2024

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zbMATH Keywords

physics-informed neural network

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long-time integration

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partial differential equation

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predictive modeling

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nonlinear dynamics

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MaRDI profile type

Publication

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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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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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On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

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A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems

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

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

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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

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Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations

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

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

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

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Physics-informed variational inference for uncertainty quantification of stochastic differential equations

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Neural‐network‐based approximations for solving partial differential equations

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A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

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PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

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Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

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Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network

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Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations

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

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

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A cusp-capturing PINN for elliptic interface problems

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A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks

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Numerical Methods for Fluid Dynamics

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Identifiers

DOI

10.1016/j.cma.2024.117036

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6557798