PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563)

scientific article; zbMATH DE number 7949564

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English	PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs	scientific article; zbMATH DE number 7949564

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scholarly article

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title

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

26 November 2024

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zbMATH Keywords

reduced order modeling

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deep learning

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physics-informed machine learning

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pretraining

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nonlinear parametrized partial differential equations

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MaRDI profile type

Publication

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cites work

Model Reduction and Approximation

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System- and Data-Driven Methods and Algorithms

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Reduced Basis Methods for Partial Differential Equations

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Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

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Model reduction and neural networks for parametric PDEs

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Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

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Learning high-dimensional parametric maps via reduced basis adaptive residual networks

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An artificial neural network framework for reduced order modeling of transient flows

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

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A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations

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A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

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POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

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Deep learning‐based reduced order models for the real‐time simulation of the nonlinear dynamics of microstructures

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Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition

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Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

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\textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications

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Reliable extrapolation of deep neural operators informed by physics or sparse observations

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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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Physics-informed machine learning for reduced-order modeling of nonlinear 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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Deep learning of thermodynamics-aware reduced-order models from data

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Structure-preserving neural networks

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A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

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Q5270493

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Solving high-dimensional partial differential equations using deep learning

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En-DeepONet: an enrichment approach for enhancing the expressivity of neural operators with applications to seismology

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Error estimates for DeepONets: a deep learning framework in infinite dimensions

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The approximation of one matrix by another of lower rank.

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A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code

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

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Physics-informed neural networks with adaptive localized artificial viscosity

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Discontinuity computing using physics-informed neural networks

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

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Improved architectures and training algorithms for deep operator networks

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2024.117404

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Sitelinks

Mathematics(1 entry)

mardi Publication:6643563