Cover Image

Generating synthetic data for neural operators

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hasani, Erisa, Ward, Rachel A.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-informed neural networks for operator equations with stochastic data
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)

Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025)

Dilated convolution neural operator for multiscale partial differential equations
by: Xu, Bo, et al.
Published: (2024)

Orthogonal greedy algorithm for linear operator learning with shallow neural network
by: Lin, Ye, et al.
Published: (2025)

Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
by: Benitez, J. Antonio Lara, et al.
Published: (2023)

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)

Generalizing the SINDy approach with nested neural networks
by: Fiorini, Camilla, et al.
Published: (2024)

Mixture of neural operator experts for learning boundary conditions and model selection
by: Deighan, Dwyer, et al.
Published: (2025)

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)

Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
by: Dimarco, Giacomo, et al.
Published: (2025)

Generalization Bounds for Sparse Random Feature Expansions
by: Hashemi, Abolfazl, et al.
Published: (2021)

Deciphering and integrating invariants for neural operator learning with various physical mechanisms
by: Zhang, Rui, et al.
Published: (2023)

A novel data generation scheme for surrogate modelling with deep operator networks
by: Choubey, Shivam, et al.
Published: (2024)

Approximation properties of neural ODEs
by: De Marinis, Arturo, et al.
Published: (2025)

Gradient-free neural topology optimization: Towards effective fracture-resistant designs
by: Kus, Gawel, et al.
Published: (2024)

Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
by: Katende, Ronald
Published: (2024)

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
by: Li, Hongyan, et al.
Published: (2022)

Training Hamiltonian neural networks without backpropagation
by: Rahma, Atamert, et al.
Published: (2024)

Gradient-free training of recurrent neural networks
by: Bolager, Erik Lien, et al.
Published: (2024)

Stable neural networks and connections to continuous dynamical systems
by: Ehrhardt, Matthias J., et al.
Published: (2025)

Energy stable neural network for gradient flow equations
by: Wu, Yue, et al.
Published: (2023)

Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)

Exact and approximate error bounds for physics-informed neural networks
by: Chantada, Augusto T., et al.
Published: (2024)

Physics-informed reduced order model with conditional neural fields
by: Kim, Minji, et al.
Published: (2024)

Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)

Nonlinear model reduction for operator learning
by: Eivazi, Hamidreza, et al.
Published: (2024)

A note on the adjoint method for neural ordinary differential equation network
by: Hu, Pipi
Published: (2024)

Two-hidden-layer ReLU neural networks and finite elements
by: Jin, Pengzhan
Published: (2024)

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
by: Rowan, Conor, et al.
Published: (2025)

DPG loss functions for learning parameter-to-solution maps by neural networks
by: Castillo, Pablo Cortés, et al.
Published: (2025)

Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
by: Li, Hongyan, et al.
Published: (2024)

Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
by: Heinlein, Alexander, et al.
Published: (2025)

Memorization capacity of deep ReLU neural networks characterized by width and depth
by: Yang, Xin, et al.
Published: (2026)

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)

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

Learning cardiac activation and repolarization times with operator learning
by: Centofanti, Edoardo, et al.
Published: (2025)

Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
by: Schotthöfer, Steffen, et al.
Published: (2024)

A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics
by: Jin, Tianyu, et al.
Published: (2024)

On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)