Efficient kernel surrogates for neural network-based regression
Fuente:
arXiv
Saved in:
| Main Authors: | Qadeer, Saad, Engel, Andrew, Howard, Amanda, Tsou, Adam, Vargas, Max, Stinis, Panos, Chiang, Tony |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
by: Heinlein, Alexander, et al.
Published: (2024)
by: Heinlein, Alexander, et al.
Published: (2024)
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
by: Cooley, Madison, et al.
Published: (2024)
by: Cooley, Madison, et al.
Published: (2024)
S$^2$GPT-PINNs: Sparse and Small models for PDEs
by: Ji, Yajie, et al.
Published: (2025)
by: Ji, Yajie, et al.
Published: (2025)
Improving the accuracy of physics-informed neural networks via last-layer retraining
by: Qadeer, Saad, et al.
Published: (2026)
by: Qadeer, Saad, et al.
Published: (2026)
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
by: Visentin, Gabriele, et al.
Published: (2025)
by: Visentin, Gabriele, 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)
Enhancing classification accuracy through chaos
by: Stinis, Panos
Published: (2026)
by: Stinis, Panos
Published: (2026)
Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
by: Caldana, Matteo, et al.
Published: (2024)
by: Caldana, Matteo, et al.
Published: (2024)
Scientific machine learning for closure models in multiscale problems: a review
by: Sanderse, Benjamin, et al.
Published: (2024)
by: Sanderse, Benjamin, et al.
Published: (2024)
ARDO: A Weak Formulation Deep Neural Network Method for Elliptic and Parabolic PDEs Based on Random Differences of Test Functions
by: Cai, Wei, et al.
Published: (2025)
by: Cai, Wei, et al.
Published: (2025)
Error estimates of asymptotic-preserving neural networks in approximating stochastic linearized Boltzmann equation
by: Wan, Jiayu, et al.
Published: (2025)
by: Wan, Jiayu, et al.
Published: (2025)
Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
by: Codega, Gabriele, et al.
Published: (2025)
by: Codega, Gabriele, 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)
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
by: Jiao, Yuling, et al.
Published: (2023)
by: Jiao, Yuling, et al.
Published: (2023)
Regularity Analysis and Tensor Neural Network Methods for Quasiperiodic Elliptic Equations
by: Ren, Jingze, et al.
Published: (2026)
by: Ren, Jingze, et al.
Published: (2026)
Solving High Dimensional Partial Differential Equations Using Tensor Neural Network and A Posteriori Error Estimators
by: Wang, Yifan, et al.
Published: (2023)
by: Wang, Yifan, et al.
Published: (2023)
Neural Network Element Method for Partial Differential Equations
by: Wang, Yifan, et al.
Published: (2025)
by: Wang, Yifan, et al.
Published: (2025)
A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
by: Patsatzis, Dimitrios G., et al.
Published: (2024)
by: Patsatzis, Dimitrios G., et al.
Published: (2024)
How Can Deep Neural Networks Fail Even With Global Optima?
by: Guan, Qingguang
Published: (2024)
by: Guan, Qingguang
Published: (2024)
A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
by: Chen, Gaohang, et al.
Published: (2025)
by: Chen, Gaohang, et al.
Published: (2025)
Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
by: Sittoni, Pietro, et al.
Published: (2026)
by: Sittoni, Pietro, et al.
Published: (2026)
Error bounds for Physics Informed Neural Networks in Generalized KdV Equations placed on unbounded domains
by: Freire, Ricardo, et al.
Published: (2026)
by: Freire, Ricardo, et al.
Published: (2026)
The lowest-order Neural Approximated Virtual Element Method on polygonal elements
by: Berrone, Stefano, et al.
Published: (2024)
by: Berrone, Stefano, et al.
Published: (2024)
Data-driven computation for periodic stochastic differential equations
by: Li, Yao, et al.
Published: (2025)
by: Li, Yao, et al.
Published: (2025)
Neural network Approximations for Reaction-Diffusion Equations -- Homogeneous Neumann Boundary Conditions and Long-time Integrations
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
Autoencoders in Function Space
by: Bunker, Justin, et al.
Published: (2024)
by: Bunker, Justin, et al.
Published: (2024)
Dimensionality reduction and width of deep neural networks based on topological degree theory
by: Yang, Xiao-Song
Published: (2025)
by: Yang, Xiao-Song
Published: (2025)
Computational homogenization for aerogel-like polydisperse open-porous materials using neural network--based surrogate models on the microscale
by: Klawonn, Axel, et al.
Published: (2024)
by: Klawonn, Axel, et al.
Published: (2024)
Deep asymptotic expansion method for solving singularly perturbed time-dependent reaction-advection-diffusion equations
by: Zhu, Qiao, et al.
Published: (2025)
by: Zhu, Qiao, et al.
Published: (2025)
Subspace method based on neural networks for eigenvalue problems
by: Dai, Xiaoying, et al.
Published: (2024)
by: Dai, Xiaoying, et al.
Published: (2024)
Convexity and strict convexity for compositional neural networks in high-dimensional optimal control
by: Grüne, Lars, et al.
Published: (2025)
by: Grüne, Lars, et al.
Published: (2025)
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
by: Li, Li'ang, et al.
Published: (2023)
by: Li, Li'ang, et al.
Published: (2023)
Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems
by: Duan, Yifei, et al.
Published: (2022)
by: Duan, Yifei, et al.
Published: (2022)
Achieving Universal Approximation and Universal Interpolation via Nonlinearity of Control Families
by: Cai, Yongqiang, et al.
Published: (2025)
by: Cai, Yongqiang, et al.
Published: (2025)
Solving Schrödinger Equation Using Tensor Neural Network
by: Liao, Yangfei, et al.
Published: (2022)
by: Liao, Yangfei, et al.
Published: (2022)
All Equalities Are Equal, but Some Are More Equal Than Others: The Effect of Implementation Aliasing on the Numerical Solution to Conservation Equations
by: Trojak, Will, et al.
Published: (2019)
by: Trojak, Will, et al.
Published: (2019)
A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
by: Ko, Gyounghun, et al.
Published: (2026)
by: Ko, Gyounghun, et al.
Published: (2026)
Error bounds for Physics Informed Neural Networks in Nonlinear Schrödinger equations placed on unbounded domains
by: Alejo, Miguel Á., et al.
Published: (2024)
by: Alejo, Miguel Á., et al.
Published: (2024)
Enriched Physics-informed Neural Networks for Dynamic Poisson-Nernst-Planck Systems
by: Huang, Xujia, et al.
Published: (2024)
by: Huang, Xujia, et al.
Published: (2024)
Stratified Sampling Algorithms for Machine Learning Methods in Solving Two-scale Partial Differential Equations
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
Similar Items
-
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
by: Heinlein, Alexander, et al.
Published: (2024) -
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
by: Cooley, Madison, et al.
Published: (2024) -
S$^2$GPT-PINNs: Sparse and Small models for PDEs
by: Ji, Yajie, et al.
Published: (2025) -
Improving the accuracy of physics-informed neural networks via last-layer retraining
by: Qadeer, Saad, et al.
Published: (2026) -
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
by: Visentin, Gabriele, et al.
Published: (2025)