GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (Q6643617)

scientific article; zbMATH DE number 7949607

Language	Label	Description	Also known as
English	GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications	scientific article; zbMATH DE number 7949607

Statements

instance of

scholarly article

0 references

title

GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (English)

0 references

0 references

0 references

0 references

Computer Methods in Applied Mechanics and Engineering

0 references

publication date

26 November 2024

0 references

zbMATH Keywords

graph neural networks

0 references

model order reduction

0 references

operator learning

0 references

resolution invariance

0 references

multifidelity surrogate modelling

0 references

parameterised PDEs

0 references

MaRDI profile type

Publication

0 references

cites work

Model Reduction and Approximation

0 references

Decay of the Kolmogorov \(N\)-width for wave problems

0 references

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

0 references

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

0 references

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

0 references

Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method

0 references

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

0 references

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

0 references

Data-driven operator inference for nonintrusive projection-based model reduction

0 references

Reduced Operator Inference for Nonlinear Partial Differential Equations

0 references

Learning nonlinear reduced models from data with operator inference

0 references

Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities

0 references

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

0 references

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

0 references

Multi-fidelity surrogate modeling using long short-term memory networks

0 references

Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

0 references

Non-intrusive reduced order modeling of nonlinear problems using neural networks

0 references

Nonlinear model order reduction based on local reduced-order bases

0 references

Nonlinear dimensionality reduction for parametric problems: a kernel Proper Orthogonal Decomposition (kPOD)

0 references

The Shifted Proper Orthogonal Decomposition: A Mode Decomposition for Multiple Transport Phenomena

0 references

A Registration Method for Model Order Reduction: Data Compression and Geometry Reduction

0 references

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

0 references

Data-driven reduced order modeling for time-dependent problems

0 references

Model order reduction for large-scale structures with local nonlinearities

0 references

Deep learning of thermodynamics-aware reduced-order models from data

0 references

Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks

0 references

Inferring solutions of differential equations using noisy multi-fidelity data

0 references

Predicting the output from a complex computer code when fast approximations are available

0 references

Kernels for vector-valued functions: a review

0 references

A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data

0 references

Multilevel Accelerated Quadrature for PDEs with Log-Normally Distributed Diffusion Coefficient

0 references

Multilevel tensor approximation of PDEs with random data

0 references

A multi-level procedure for enhancing accuracy of machine learning algorithms

0 references

Hidden physics models: machine learning of nonlinear partial differential equations

0 references

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

0 references

Deep learning of vortex-induced vibrations

0 references

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

0 references

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

0 references

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

0 references

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

0 references

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

0 references

Physics-informed machine learning for reduced-order modeling of nonlinear problems

0 references

Multidimensional binary search trees used for associative searching

0 references

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

0 references

Real time reduced order computational mechanics. Parametric PDEs worked out problems (to appear)

0 references

Automated solution of differential equations by the finite element method. The FEniCS book

0 references

A comparison of neural network architectures for data-driven reduced-order modeling

0 references

Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations

0 references

Identifiers

Mathematics Subject Classification ID

0 references

0 references

10.1016/J.CMA.2024.117458

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6643617