NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315)

scientific article; zbMATH DE number 7967162

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English	NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements	scientific article; zbMATH DE number 7967162

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instance of

scholarly article

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title

NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (English)

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George Em Karniadakis

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published in

Computer Methods in Applied Mechanics and Engineering

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publication date

14 January 2025

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zbMATH Keywords

physics-informed machine learning

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PINNs

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spectral element method

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data assimilation

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PIV

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multiphysics problems

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heat transfer

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domain decomposition

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MaRDI profile type

Publication

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cites work

Handbook of Spatial Statistics

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Data Assimilation

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

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The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics

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

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A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data

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Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression

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Correcting model misspecification in physics-informed neural networks (PINNs)

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

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Nektar++: an open-source spectral/\(hp\) element framework

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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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Bayesian physics informed neural networks for real-world nonlinear dynamical systems

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Physics informed neural networks for continuum micromechanics

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NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators

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Physics-informed neural networks for high-speed flows

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Parallel physics-informed neural networks via domain decomposition

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Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

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Physics-Informed Neural Networks with Hard Constraints for Inverse Design

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

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Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Large-Scale Machine Learning with Stochastic Gradient Descent

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

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Spectral/hp Element Methods for Computational Fluid Dynamics

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High-order splitting methods for the incompressible Navier-Stokes equations

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A spectral element method for fluid dynamics: Laminar flow in a channel expansion

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High-Order Methods for Incompressible Fluid Flow

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Q4558473

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Numerical simulation of two-dimensional Rayleigh-Bénard convection in an enclosure

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Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

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Self-adaptive physics-informed neural networks

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2024.117498

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Sitelinks

Mathematics(1 entry)

mardi Publication:6663315