Imagen de Portada

Size Lowerbounds for Deep Operator Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mukherjee, Anirbit, Roy, Amartya
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Computational Complexity Numerical Analysis Analysis of PDEs
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
por: Kumar, Dibyakanti, et al.
Publicado: (2023)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
por: Andre-Sloan, Sebastien, et al.
Publicado: (2026)

Towards Size-Independent Generalization Bounds for Deep Operator Nets
por: Gopalani, Pulkit, et al.
Publicado: (2022)

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
por: Huang, Chih-Kang, et al.
Publicado: (2026)

What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation?
por: Xu, Ruihan, et al.
Publicado: (2025)

An interface crack in 1d piezoelectric quasicrystal under antiplane mechanical loading and electric field
por: Altoumaimi, Mohammed, et al.
Publicado: (2025)

High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise
por: Fang, Yuchen, et al.
Publicado: (2025)

A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization
por: Liu, Wei, et al.
Publicado: (2025)

On Approximate Computation of Critical Points
por: Ahmadi, Amir Ali, et al.
Publicado: (2026)

Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions
por: Chen, Ziang, et al.
Publicado: (2025)

Wasserstein Bounds for generative diffusion models with Gaussian tail targets
por: Wang, Xixian, et al.
Publicado: (2024)

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
por: Wang, Yixuan
Publicado: (2026)

Regularity of Second-Order Elliptic PDEs in Spectral Barron Spaces
por: Chen, Ziang, et al.
Publicado: (2026)

Scalable Signature Kernel Computations for Long Time Series via Local Neumann Series Expansions
por: Tamayo-Rios, Matthew, et al.
Publicado: (2025)

Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
por: Ren, Mohan, et al.
Publicado: (2024)

Regularized Stein Variational Gradient Flow
por: He, Ye, et al.
Publicado: (2022)

One model to solve them all: 2BSDE families via neural operators
por: Furuya, Takashi, et al.
Publicado: (2025)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
por: Hwang, Youngsik, et al.
Publicado: (2024)

Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
por: Farkane, Ayoub, et al.
Publicado: (2026)

Space-time deep neural network approximations for high-dimensional partial differential equations
por: Hornung, Fabian, et al.
Publicado: (2020)

Hermite Neural Network Simulation for Solving the 2D Schrodinger Equation
por: Parand, Kourosh, et al.
Publicado: (2024)

Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
por: Mukherjee, Amartya, et al.
Publicado: (2024)

Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers
por: Haight, Chandler, et al.
Publicado: (2025)

On the power of adaption and randomization
por: Krieg, David, et al.
Publicado: (2024)

Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
por: Mukherjee, Amartya, et al.
Publicado: (2026)

A physics-informed neural network framework for modeling obstacle-related equations
por: Bahja, Hamid El, et al.
Publicado: (2023)

Physics-informed fine-tuning of foundation models for partial differential equations
por: Medvedev, Vlad, et al.
Publicado: (2026)

SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
por: Su, Hua, et al.
Publicado: (2025)

Latent Space Element Method
por: Chung, Seung Whan, et al.
Publicado: (2026)

Deterministic constructions of high-dimensional sets with small dispersion
por: Ullrich, Mario, et al.
Publicado: (2019)

A PDE Perspective on Approximating Nonlocal Periodic Operators with Applications on Neural Networks for Critical SQG Equations
por: Abdo, Elie, et al.
Publicado: (2024)

Solving Inverse Acoustic Obstacle Scattering Problem with Phaseless Far-Field Measurement Using Deep Neural Network Surrogates
por: Fan, Yuxin, et al.
Publicado: (2025)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
por: Jiang, Zhaoxi, et al.
Publicado: (2025)

A Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) Framework for Non-linear Finite Element Analysis
por: Liu, Yingjian, et al.
Publicado: (2025)

Numerical Schemes for Signature Kernels
por: Cass, Thomas, et al.
Publicado: (2025)

Models for information propagation on graphs
por: Dunbar, Oliver R. A., et al.
Publicado: (2022)

From Monte Carlo to neural networks approximations of boundary value problems
por: Beznea, Lucian, et al.
Publicado: (2022)

Numerical Treatment of Non-local Integral Operators in the Framework of Evolutionary Equations
por: Franz, Sebastian, et al.
Publicado: (2026)

Approximating Analytic Spectra of Hyperbolic Systems with Summation-by-Parts Finite Difference Operators
por: Erickson, Brittany A.
Publicado: (2024)

Physics-informed Discretization-independent Deep Compositional Operator Network
por: Zhong, Weiheng, et al.
Publicado: (2024)