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

Cites Work

Title not available (Why is that?)
A spectral element method for fluid dynamics: Laminar flow in a channel expansion
Numerical simulation of two-dimensional Rayleigh-Bénard convection in an enclosure
High-order splitting methods for the incompressible Navier-Stokes equations
Nektar++: an open-source spectral/\(hp\) element framework
The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Self-adaptive physics-informed neural networks
NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
Parallel physics-informed neural networks via domain decomposition
Physics informed neural networks for continuum micromechanics
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Scientific machine learning through physics-informed neural networks: where we are and what's next
Physics-informed neural networks for high-speed flows
Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Bayesian physics informed neural networks for real-world nonlinear dynamical systems
Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
Large-Scale Machine Learning with Stochastic Gradient Descent
Handbook of Spatial Statistics
High-Order Methods for Incompressible Fluid Flow
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
DeepXDE: A Deep Learning Library for Solving Differential Equations
Physics-Informed Neural Networks with Hard Constraints for Inverse Design
Spectral/hp Element Methods for Computational Fluid Dynamics
Data Assimilation
A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems
Correcting model misspecification in physics-informed neural networks (PINNs)
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning

This page was built for publication: NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6663315)