Adaptive task decomposition physics-informed neural networks (Q6120149)

scientific article; zbMATH DE number 7823444

Language	Label	Description	Also known as
English	Adaptive task decomposition physics-informed neural networks	scientific article; zbMATH DE number 7823444

Statements

instance of

scholarly article

0 references

title

Adaptive task decomposition physics-informed neural networks (English)

0 references

published in

Computer Methods in Applied Mechanics and Engineering

0 references

publication date

25 March 2024

0 references

zbMATH Keywords

physics-informed neural networks (PINNs)

0 references

task decomposition

0 references

progressive learning

0 references

minibatch method

0 references

MaRDI profile type

MaRDI publication profile

0 references

cites work

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

0 references

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

0 references

Physics-informed neural networks for high-speed flows

0 references

A deep learning energy method for hyperelasticity and viscoelasticity

0 references

SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

0 references

Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology

0 references

Model-free data-driven inelasticity

0 references

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

0 references

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

0 references

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

0 references

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

0 references

Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response

0 references

Self-adaptive physics-informed neural networks

0 references

DeepXDE: A Deep Learning Library for Solving Differential Equations

0 references

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

0 references

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

0 references

Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks

0 references

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

0 references

The mixed deep energy method for resolving concentration features in finite strain hyperelasticity

0 references

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

0 references

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

0 references

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

0 references

Computational inelasticity

0 references

Large Sample Properties of Simulations Using Latin Hypercube Sampling

0 references

A proposal on machine learning via dynamical systems

0 references

Identifiers

DOI

10.1016/j.cma.2023.116561

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6120149