Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274)

scientific article; zbMATH DE number 7897547

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English	Variational temporal convolutional networks for I-FENN thermoelasticity	scientific article; zbMATH DE number 7897547

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instance of

scholarly article

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title

Variational temporal convolutional networks for I-FENN thermoelasticity (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

15 August 2024

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zbMATH Keywords

multiphysics

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physics-informed neural networks (PINNs)

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Seq2Seq learning

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transient analysis

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variational methods

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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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Scientific machine learning through physics-informed neural networks: where we are and what's next

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Deep energy method in topology optimization applications

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Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

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Physics informed neural networks for continuum micromechanics

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The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

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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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A deep energy method for finite deformation hyperelasticity

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Parametric deep energy approach for elasticity accounting for strain gradient effects

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A deep learning energy method for hyperelasticity and viscoelasticity

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Investigating deep energy method applications in thermoelasticity

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Physics-Informed Neural Networks with Hard Constraints for Inverse Design

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

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fPINNs: Fractional Physics-Informed Neural Networks

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Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains

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Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths

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Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics

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Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance

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Multilayer feedforward networks are universal approximators

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Q4938227

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Deep vs. shallow networks: an approximation theory perspective

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Approximation by superpositions of a sigmoidal function

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Thermoelasticity and entropy flow

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Thermoelasticity and Irreversible Thermodynamics

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A variational principle for the coupled thermoelastic problem

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Generalised thermoelasticity

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An unconditionally stable staggered algorithm for transient finite element analysis of coupled thermoelastic problems

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Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

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Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

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I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation

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Q2934059

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Deep Learning with Python

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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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Algorithm 799: revolve

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Evaluating Derivatives

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On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method

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

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On the limited memory BFGS method for large scale optimization

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Nonsmooth optimization via quasi-Newton methods

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Meshless physics-informed deep learning method for three-dimensional solid mechanics

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

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Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent?

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On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

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

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Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2024.117122

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Sitelinks

Mathematics(1 entry)

mardi Publication:6588274