Provably accurate adaptive sampling for collocation points in physics-informed neural networks
- URL: http://arxiv.org/abs/2504.00910v1
- Date: Tue, 01 Apr 2025 15:45:08 GMT
- Title: Provably accurate adaptive sampling for collocation points in physics-informed neural networks
- Authors: Antoine Caradot, Rémi Emonet, Amaury Habrard, Abdel-Rahim Mezidi, Marc Sebban,
- Abstract summary: Physics-informed Neural Networks (PINN) have emerged as an efficient way to learn surrogate solvers.<n>We introduce a provably accurate sampling method for collocation points based on the Hessian of the PDE residuals.
- Score: 11.912466054588327
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite considerable scientific advances in numerical simulation, efficiently solving PDEs remains a complex and often expensive problem. Physics-informed Neural Networks (PINN) have emerged as an efficient way to learn surrogate solvers by embedding the PDE in the loss function and minimizing its residuals using automatic differentiation at so-called collocation points. Originally uniformly sampled, the choice of the latter has been the subject of recent advances leading to adaptive sampling refinements for PINNs. In this paper, leveraging a new quadrature method for approximating definite integrals, we introduce a provably accurate sampling method for collocation points based on the Hessian of the PDE residuals. Comparative experiments conducted on a set of 1D and 2D PDEs demonstrate the benefits of our method.
Related papers
- PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks [44.99833362998488]
PINNs minimize a loss function which includes the PDE residual determined for a set of collocation points.<n>Previous work has shown that the number and distribution of these collocation points have a significant influence on the accuracy of the PINN solution.<n>We present the Point Adaptive Collocation Method for Artificial Neural Networks (PACMANN)
arXiv Detail & Related papers (2024-11-29T11:31:11Z) - Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models [50.90868087591973]
We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models.
We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a non-linear diffusion equation.
arXiv Detail & Related papers (2024-08-20T19:06:02Z) - Dynamical Measure Transport and Neural PDE Solvers for Sampling [77.38204731939273]
We tackle the task of sampling from a probability density as transporting a tractable density function to the target.
We employ physics-informed neural networks (PINNs) to approximate the respective partial differential equations (PDEs) solutions.
PINNs allow for simulation- and discretization-free optimization and can be trained very efficiently.
arXiv Detail & Related papers (2024-07-10T17:39:50Z) - RoPINN: Region Optimized Physics-Informed Neural Networks [66.38369833561039]
Physics-informed neural networks (PINNs) have been widely applied to solve partial differential equations (PDEs)
This paper proposes and theoretically studies a new training paradigm as region optimization.
A practical training algorithm, Region Optimized PINN (RoPINN), is seamlessly derived from this new paradigm.
arXiv Detail & Related papers (2024-05-23T09:45:57Z) - Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs [2.526490864645154]
We propose a new minmax formulation to optimize simultaneously the approximate solution, given by a neural network model, and the random samples in the training set.
The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile.
arXiv Detail & Related papers (2023-05-30T02:59:18Z) - Implicit Stochastic Gradient Descent for Training Physics-informed
Neural Networks [51.92362217307946]
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems.
PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features.
In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
arXiv Detail & Related papers (2023-03-03T08:17:47Z) - A Novel Adaptive Causal Sampling Method for Physics-Informed Neural
Networks [35.25394937917774]
Informed Neural Networks (PINNs) have become a kind of attractive machine learning method for obtaining solutions of partial differential equations (PDEs)
We introduce temporal causality into adaptive sampling and propose a novel adaptive causal sampling method to improve the performance and efficiency of PINs.
We demonstrate that by utilizing such a relatively simple sampling method, prediction performance can be improved up to two orders of magnitude compared with state-of-the-art results.
arXiv Detail & Related papers (2022-10-24T01:51:08Z) - Lie Point Symmetry Data Augmentation for Neural PDE Solvers [69.72427135610106]
We present a method, which can partially alleviate this problem, by improving neural PDE solver sample complexity.
In the context of PDEs, it turns out that we are able to quantitatively derive an exhaustive list of data transformations.
We show how it can easily be deployed to improve neural PDE solver sample complexity by an order of magnitude.
arXiv Detail & Related papers (2022-02-15T18:43:17Z) - DAS: A deep adaptive sampling method for solving partial differential
equations [2.934397685379054]
We propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs)
Deep neural networks are utilized to approximate the solutions of PDEs and deep generative models are employed to generate new collocation points that refine the training set.
We present a theoretical analysis to show that the proposed DAS method can reduce the error bound and demonstrate its effectiveness with numerical experiments.
arXiv Detail & Related papers (2021-12-28T08:37:47Z) - Efficient training of physics-informed neural networks via importance
sampling [2.9005223064604078]
Physics-In Neural Networks (PINNs) are a class of deep neural networks that are trained to compute systems governed by partial differential equations (PDEs)
We show that an importance sampling approach will improve the convergence behavior of PINNs training.
arXiv Detail & Related papers (2021-04-26T02:45:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.