Efficiently training physics-informed neural networks via anomaly-aware optimization

Cites Work

Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation
Self-adaptive physics-informed neural networks
SelectNet: self-paced learning for high-dimensional partial differential equations
When and why PINNs fail to train: a neural tangent kernel perspective
Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Numerical treatment of partial differential equations. Revised translation of the 3rd German edition of `Numerische Behandlung partieller Differentialgleichungen' by Martin Stynes.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations
A New Artificial Neural Network Method for Solving Schrödinger Equations on Unbounded Domains
An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions
Deep Unfitted Nitsche Method for Elliptic Interface Problems
Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow
DeepXDE: A Deep Learning Library for Solving Differential Equations
A Local Deep Learning Method for Solving High Order Partial Differential Equations
Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems
A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics
Linearized Learning with Multiscale Deep Neural Networks for Stationary Navier-Stokes Equations with Oscillatory Solutions
Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs)
Learning Specialized Activation Functions for Physics-Informed Neural Networks
Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs
MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling
HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions
A Discontinuity and Cusp Capturing PINN for Stokes Interface Problems with Discontinuous Viscosity and Singular Forces
Failure-Informed Adaptive Sampling for PINNs
Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems

This page was built for publication: Efficiently training physics-informed neural networks via anomaly-aware optimization

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6662394)