Spectral operator learning for parametric PDEs without data reliance
From MaRDI portal
Publication:6194143
DOI10.1016/j.cma.2023.116678arXiv2310.02013MaRDI QIDQ6194143
Namjung Kim, Youngjoon Hong, Junho Choi, Taehyun Yun
Publication date: 19 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2310.02013
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Fluid dynamics in group T-3 Los Alamos National Laboratory: (LA-UR-03-3852)
- Exponential time differencing for stiff systems
- Learning constitutive relations from indirect observations using deep neural networks
- Enriched numerical scheme for singularly perturbed barotropic quasi-geostrophic equations
- The route to chaos for the Kuramoto-Sivashinsky equation
- Mathematical methods for financial markets.
- Enriched spectral method for stiff convection-dominated equations
- Singular perturbations and boundary layers
- Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- PPINN: parareal physics-informed neural network for time-dependent PDEs
- A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
- Self-adaptive physics-informed neural networks
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing
- DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks
- DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
- When and why PINNs fail to train: a neural tangent kernel perspective
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Adversarial uncertainty quantification in physics-informed neural networks
- On the numerical approximations of stiff convection-diffusion equations in a circle
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
- Spectral Methods
- Spectral Methods in MATLAB
- Finite Volume Methods for Hyperbolic Problems
- Solving high-dimensional partial differential equations using deep learning
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- An in-depth numerical study of the two-dimensional Kuramoto–Sivashinsky equation
- Invariant recurrent solutions embedded in a turbulent two-dimensional Kolmogorov flow
- Calculation of Gauss Quadrature Rules
This page was built for publication: Spectral operator learning for parametric PDEs without data reliance
