Single-shot prediction of parametric partial differential equations
Fuente:
arXiv
Saved in:
| Main Authors: | Rafiq, Khalid, Liao, Wenjing, Nair, Aditya G. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
by: Kashi, Aditya, et al.
Published: (2024)
by: Kashi, Aditya, et al.
Published: (2024)
Approximation theory for 1-Lipschitz ResNets
by: Murari, Davide, et al.
Published: (2025)
by: Murari, Davide, et al.
Published: (2025)
Embedding Inequalities for Barron-type Spaces
by: Wu, Lei
Published: (2023)
by: Wu, Lei
Published: (2023)
Learning to Control the Smoothness of Graph Convolutional Network Features
by: Wang, Shih-Hsin, et al.
Published: (2024)
by: Wang, Shih-Hsin, et al.
Published: (2024)
Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations
by: Sommer, Finn, et al.
Published: (2026)
by: Sommer, Finn, et al.
Published: (2026)
A scaled TW-PINN: A physics-informed neural network for traveling wave solutions of reaction-diffusion equations with general coefficients
by: Han, Seungwan, et al.
Published: (2026)
by: Han, Seungwan, et al.
Published: (2026)
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
by: Wang, Shih-Hsin, et al.
Published: (2026)
by: Wang, Shih-Hsin, et al.
Published: (2026)
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
by: Shen, Haoran, et al.
Published: (2024)
by: Shen, Haoran, et al.
Published: (2024)
Improving Flow Matching by Aligning Flow Divergence
by: Huang, Yuhao, et al.
Published: (2026)
by: Huang, Yuhao, et al.
Published: (2026)
Discontinuous hybrid neural networks for the one-dimensional partial differential equations
by: Wang, Xiaoyu, et al.
Published: (2025)
by: Wang, Xiaoyu, et al.
Published: (2025)
A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)
by: Kapllani, Lorenc, et al.
Published: (2024)
A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)
by: Kapllani, Lorenc, et al.
Published: (2024)
Conservative approximation-based feedforward neural network for WENO schemes
by: Park, Kwanghyuk, et al.
Published: (2025)
by: Park, Kwanghyuk, et al.
Published: (2025)
Mathematical analysis of the gradients in deep learning
by: Dereich, Steffen, et al.
Published: (2025)
by: Dereich, Steffen, et al.
Published: (2025)
Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems
by: Li, Chunyan, et al.
Published: (2025)
by: Li, Chunyan, et al.
Published: (2025)
Enforcing boundary conditions for physics-informed neural operators
by: Göschel, Niklas, et al.
Published: (2025)
by: Göschel, Niklas, et al.
Published: (2025)
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
by: Brivio, Simone, et al.
Published: (2025)
by: Brivio, Simone, et al.
Published: (2025)
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
by: Park, Youngjae, et al.
Published: (2026)
by: Park, Youngjae, et al.
Published: (2026)
Approximation of the Proximal Operator of the $\ell_\infty$ Norm Using a Neural Network
by: Linehan, Kathryn, et al.
Published: (2024)
by: Linehan, Kathryn, et al.
Published: (2024)
Sparse Implementation of Versatile Graph-Informed Layers
by: Della Santa, Francesco
Published: (2024)
by: Della Santa, Francesco
Published: (2024)
Quantitative Approximation Rates for Group Equivariant Learning
by: Siegel, Jonathan W., et al.
Published: (2026)
by: Siegel, Jonathan W., et al.
Published: (2026)
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
by: Jentzen, Arnulf, et al.
Published: (2023)
by: Jentzen, Arnulf, et al.
Published: (2023)
Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions
by: He, Juncai, et al.
Published: (2023)
by: He, Juncai, et al.
Published: (2023)
Recurrent Neural Operators: Stable Long-Term PDE Prediction
by: Ye, Zaijun, et al.
Published: (2025)
by: Ye, Zaijun, et al.
Published: (2025)
Greedy feature selection: Classifier-dependent feature selection via greedy methods
by: Camattari, Fabiana, et al.
Published: (2024)
by: Camattari, Fabiana, et al.
Published: (2024)
To be or not to be stable, that is the question: understanding neural networks for inverse problems
by: Evangelista, Davide, et al.
Published: (2022)
by: Evangelista, Davide, et al.
Published: (2022)
Marrying Compressed Sensing and Deep Signal Separation
by: Hickok, Truman, et al.
Published: (2024)
by: Hickok, Truman, et al.
Published: (2024)
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
by: Cho, Sung Woong, et al.
Published: (2024)
by: Cho, Sung Woong, et al.
Published: (2024)
Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations
by: Ye, Zhanhong, et al.
Published: (2023)
by: Ye, Zhanhong, et al.
Published: (2023)
Learned RESESOP for solving inverse problems with inexact forward operator
by: Feinler, Mathias S., et al.
Published: (2024)
by: Feinler, Mathias S., et al.
Published: (2024)
An $r$-adaptive finite element method using neural networks for parametric self-adjoint elliptic problem
by: Aballay, Danilo, et al.
Published: (2025)
by: Aballay, Danilo, et al.
Published: (2025)
Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
by: Ragonesi, Alice, et al.
Published: (2025)
by: Ragonesi, Alice, et al.
Published: (2025)
Convergence of the generalization error for deep gradient flow methods for PDEs
by: Liu, Chenguang, et al.
Published: (2025)
by: Liu, Chenguang, et al.
Published: (2025)
On the algorithmic construction of deep ReLU networks
by: Huybrechs, Daan
Published: (2025)
by: Huybrechs, Daan
Published: (2025)
Towards Model Discovery Using Domain Decomposition and PINNs
by: Saha, Tirtho S., et al.
Published: (2024)
by: Saha, Tirtho S., et al.
Published: (2024)
Differentiable DG with Neural Operator Source Term Correction
by: Kang, Shinhoo, et al.
Published: (2023)
by: Kang, Shinhoo, et al.
Published: (2023)
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
by: Chew, Joyce, et al.
Published: (2022)
by: Chew, Joyce, et al.
Published: (2022)
Ensembles provably learn equivariance through data augmentation
by: Nordenfors, Oskar, et al.
Published: (2024)
by: Nordenfors, Oskar, et al.
Published: (2024)
Accuracy and stability of Artificial Neural Networks for HP-Splines frequency parameter selection
by: Bruni, Vittoria, et al.
Published: (2026)
by: Bruni, Vittoria, et al.
Published: (2026)
Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
by: Wu, Xiaolong, et al.
Published: (2025)
by: Wu, Xiaolong, et al.
Published: (2025)
Similar Items
-
Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
by: Kashi, Aditya, et al.
Published: (2024) -
Approximation theory for 1-Lipschitz ResNets
by: Murari, Davide, et al.
Published: (2025) -
Embedding Inequalities for Barron-type Spaces
by: Wu, Lei
Published: (2023) -
Learning to Control the Smoothness of Graph Convolutional Network Features
by: Wang, Shih-Hsin, et al.
Published: (2024) -
Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations
by: Sommer, Finn, et al.
Published: (2026)