Saved in:
| Main Authors: | Kratsios, Anastasis, Furuya, Takashi, Benitez, Jose Antonio Lara, Lassas, Matti, de Hoop, Maarten |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09101 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
by: Furuya, Takashi, et al.
Published: (2024)
by: Furuya, Takashi, et al.
Published: (2024)
Is In-Context Universality Enough? MLPs are Also Universal In-Context
by: Kratsios, Anastasis, et al.
Published: (2025)
by: Kratsios, Anastasis, et al.
Published: (2025)
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
by: Kratsios, Anastasis, et al.
Published: (2024)
by: Kratsios, Anastasis, et al.
Published: (2024)
Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
by: Kratsios, Anastasis, et al.
Published: (2025)
by: Kratsios, Anastasis, et al.
Published: (2025)
Semialgebraic Neural Networks: From roots to representations
by: Mis, S. David, et al.
Published: (2025)
by: Mis, S. David, et al.
Published: (2025)
One model to solve them all: 2BSDE families via neural operators
by: Furuya, Takashi, et al.
Published: (2025)
by: Furuya, Takashi, et al.
Published: (2025)
Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
by: Rouhvarzi, Hossein, et al.
Published: (2025)
by: Rouhvarzi, Hossein, et al.
Published: (2025)
Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs
by: Kratsios, Anastasis, et al.
Published: (2025)
by: Kratsios, Anastasis, et al.
Published: (2025)
Extension and neural operator approximation of the electrical impedance tomography inverse map
by: de Hoop, Maarten V., et al.
Published: (2025)
by: de Hoop, Maarten V., et al.
Published: (2025)
Transformers through the lens of support-preserving maps between measures
by: Furuya, Takashi, et al.
Published: (2025)
by: Furuya, Takashi, et al.
Published: (2025)
An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems
by: Schneider, Fabian, et al.
Published: (2024)
by: Schneider, Fabian, et al.
Published: (2024)
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
by: Hong, Ruiyang, et al.
Published: (2024)
by: Hong, Ruiyang, et al.
Published: (2024)
Neural Operators Can Play Dynamic Stackelberg Games
by: Alvarez, Guillermo, et al.
Published: (2024)
by: Alvarez, Guillermo, et al.
Published: (2024)
Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
by: Kratsios, Anastasis
Published: (2026)
by: Kratsios, Anastasis
Published: (2026)
Simultaneously Solving Infinitely Many LQ Mean Field Games In Hilbert Spaces: The Power of Neural Operators
by: Firoozi, Dena, et al.
Published: (2025)
by: Firoozi, Dena, et al.
Published: (2025)
Can neural operators always be continuously discretized?
by: Furuya, Takashi, et al.
Published: (2024)
by: Furuya, Takashi, et al.
Published: (2024)
A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics
by: Nabian, Mohammad Amin, et al.
Published: (2025)
by: Nabian, Mohammad Amin, et al.
Published: (2025)
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)
by: Furuya, Takashi, et al.
Published: (2024)
A Mathematical Guide to Operator Learning
by: Boullé, Nicolas, et al.
Published: (2023)
by: Boullé, Nicolas, et al.
Published: (2023)
Projection Methods for Operator Learning and Universal Approximation
by: Zappala, Emanuele
Published: (2024)
by: Zappala, Emanuele
Published: (2024)
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
by: Zhang, Ce, et al.
Published: (2023)
by: Zhang, Ce, et al.
Published: (2023)
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
by: Berner, Julius, et al.
Published: (2025)
by: Berner, Julius, et al.
Published: (2025)
Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
by: Singh, Ashutosh, et al.
Published: (2024)
by: Singh, Ashutosh, et al.
Published: (2024)
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
by: Hou, Xianglong, et al.
Published: (2025)
by: Hou, Xianglong, et al.
Published: (2025)
Function graph transformers universally approximate operators between function spaces
by: Furuya, Takashi, et al.
Published: (2026)
by: Furuya, Takashi, et al.
Published: (2026)
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)
by: Hu, Zheyuan, et al.
Published: (2023)
Operator learning without the adjoint
by: Boullé, Nicolas, et al.
Published: (2024)
by: Boullé, Nicolas, et al.
Published: (2024)
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
by: Son, Hwijae
Published: (2025)
by: Son, Hwijae
Published: (2025)
A Dimensionality Reduction Approach for Convolutional Neural Networks
by: Meneghetti, Laura, et al.
Published: (2021)
by: Meneghetti, Laura, et al.
Published: (2021)
Universal Approximation of Nonlinear Operators and Their Derivatives
by: de Feo, Filippo
Published: (2026)
by: de Feo, Filippo
Published: (2026)
Neural Operators with Localized Integral and Differential Kernels
by: Liu-Schiaffini, Miguel, et al.
Published: (2024)
by: Liu-Schiaffini, Miguel, et al.
Published: (2024)
CATO: Charted Attention for Neural PDE Operators
by: Cheng, Chun-Wun, et al.
Published: (2026)
by: Cheng, Chun-Wun, et al.
Published: (2026)
Autoregression-Free Neural Operators for Time-Dependent PDEs
by: Zhang, Jiaquan, et al.
Published: (2026)
by: Zhang, Jiaquan, et al.
Published: (2026)
Monte Carlo-Type Neural Operator for Differential Equations
by: Choutri, Salah Eddine, et al.
Published: (2025)
by: Choutri, Salah Eddine, et al.
Published: (2025)
Training Infinitely Deep and Wide Transformers
by: Barboni, Raphaël, et al.
Published: (2026)
by: Barboni, Raphaël, et al.
Published: (2026)
STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
by: Wang, Hong, et al.
Published: (2025)
by: Wang, Hong, et al.
Published: (2025)
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
by: Wang, Hong, et al.
Published: (2024)
by: Wang, Hong, et al.
Published: (2024)
Neural Operators Can Discover Functional Clusters
by: Li, Yicen, et al.
Published: (2026)
by: Li, Yicen, et al.
Published: (2026)
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach
by: Arabpour, Reza, et al.
Published: (2024)
by: Arabpour, Reza, et al.
Published: (2024)
Similar Items
-
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
by: Benitez, J. Antonio Lara, et al.
Published: (2023) -
Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
by: Furuya, Takashi, et al.
Published: (2024) -
Is In-Context Universality Enough? MLPs are Also Universal In-Context
by: Kratsios, Anastasis, et al.
Published: (2025) -
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
by: Kratsios, Anastasis, et al.
Published: (2024) -
Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
by: Kratsios, Anastasis, et al.
Published: (2025)