:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Han, Jihun, Lee, Yoonsang
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Computational Engineering, Finance, and Science Machine Learning Probability 65N99, 65C05, 68T07
Online Access:	https://arxiv.org/abs/2403.11385
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Neumann-Neumann Acceleration with Coarse Space for Domain Decomposition of Extreme Learning Machines
by: Lee, Chang-Ock, et al.
Published: (2025)

An analysis of the derivative-free loss method for solving PDEs
by: Han, Jihun, et al.
Published: (2023)

Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators
by: Lozano, Pere Díaz, et al.
Published: (2024)

Enforcing boundary conditions for physics-informed neural operators
by: Göschel, Niklas, et al.
Published: (2025)

RONOM: Reduced-Order Neural Operator Modeling
by: Dummer, Sven, et al.
Published: (2025)

How Can Deep Neural Networks Fail Even With Global Optima?
by: Guan, Qingguang
Published: (2024)

Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
by: Sittoni, Pietro, et al.
Published: (2026)

Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
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)

A Nonoverlapping Domain Decomposition Method for Extreme Learning Machines: Elliptic Problems
by: Lee, Chang-Ock, et al.
Published: (2024)

Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach
by: Kim, Yeongjong, et al.
Published: (2025)

Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers
by: Lerer, Bar, et al.
Published: (2023)

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
by: Kashi, Aditya, et al.
Published: (2024)

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
by: Tahimi, Abdeladhim
Published: (2026)

Regularity Analysis and Tensor Neural Network Methods for Quasiperiodic Elliptic Equations
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)

Neural Network Element Method for Partial Differential Equations
by: Wang, Yifan, et al.
Published: (2025)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations
by: Ackermann, Julia, et al.
Published: (2024)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
by: Ackermann, Julia, et al.
Published: (2023)

Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
by: Chen, Yifan, et al.
Published: (2022)

PODNO: Proper Orthogonal Decomposition Neural Operators
by: Cheng, Zilan, et al.
Published: (2025)

Deep Micro Solvers for Rough-Wall Stokes Flow in a Heterogeneous Multiscale Method
by: Ström, Emanuel, et al.
Published: (2025)

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
by: Park, Youngjae, et al.
Published: (2026)

A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
by: Chen, Gaohang, et al.
Published: (2025)

Physics-informed low-rank neural operators with application to parametric elliptic PDEs
by: Schaffer, Sebastian, et al.
Published: (2025)

Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky
by: Epperly, Ethan N., et al.
Published: (2024)

Scalable solvers for complex electromagnetics problems
by: Badia, Santiago, et al.
Published: (2019)

An Euler scheme for BSDEs via the Wiener chaos decomposition
by: Lozano, Pere Díaz, et al.
Published: (2025)

Deep asymptotic expansion method for solving singularly perturbed time-dependent reaction-advection-diffusion equations
by: Zhu, Qiao, et al.
Published: (2025)

Deep-Picard Iteration for Space-time Fractional Diffusion PDEs
by: Zeng, Zhijun, et al.
Published: (2026)

The lowest-order Neural Approximated Virtual Element Method on polygonal elements
by: Berrone, Stefano, et al.
Published: (2024)

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
by: Codega, Gabriele, et al.
Published: (2025)

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
by: Georgoulis, Emmanuil H., 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)

BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers
by: Zhang, Rui, et al.
Published: (2025)

Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
by: Khamlich, Moaad, et al.
Published: (2023)

Gaussian Variational Schemes on Bounded and Unbounded Domains
by: Actor, Jonas A., et al.
Published: (2024)

S$^2$GPT-PINNs: Sparse and Small models for PDEs
by: Ji, Yajie, et al.
Published: (2025)

Arbitrary-order pressure-robust DDR and VEM methods for the Stokes problem on polyhedral meshes
by: da Veiga, Lourenço Beirão, et al.
Published: (2021)

Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2025)