Stochastic projection based approach for gradient free physics informed learning (Q2686876)

scientific article

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English	Stochastic projection based approach for gradient free physics informed learning	scientific article

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instance of

scholarly article

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title

Stochastic projection based approach for gradient free physics informed learning (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

1 March 2023

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full work available at URL

https://arxiv.org/abs/2209.13724

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zbMATH Keywords

stochastic projection

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physics informed neural network

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partial differential equations

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automatic differentiation

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loss function

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describes a project that uses

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MaRDI publication profile

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cites work

Finite Volume Methods for Hyperbolic Problems

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Implementing Spectral Methods for 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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Transfer learning based multi-fidelity physics informed deep neural network

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

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Neural‐network‐based approximations for solving partial differential equations

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SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs

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DGM: a deep learning algorithm for solving partial differential equations

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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Parametric deep energy approach for elasticity accounting for strain gradient effects

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CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method

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

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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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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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Physics-Informed Neural Networks with Hard Constraints for Inverse Design

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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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Q4558473

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On higher gradients in continuum-atomistic modelling.

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Estimation of Stochastic Systems: Arbitrary System Process with Additive White Noise Observation Errors

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On Transforming a Certain Class of Stochastic Processes by Absolutely Continuous Substitution of Measures

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Spectral/hp Element Methods for Computational Fluid Dynamics

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A phase field model for rate-independent crack propagation: robust algorithmic implementation based on operator splits

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Thermodynamically consistent phase-field models of fracture: Variational principles and multi-field FE implementations

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Eigenfracture: An Eigendeformation Approach to Variational Fracture

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Numerical experiments in revisited brittle fracture

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A phase-field description of dynamic brittle fracture

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Identifiers

DOI

10.1016/j.cma.2022.115842

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:2686876