Leray-Schauder Mappings for Operator Learning
Fuente:
arXiv
Saved in:
| Main Author: | Zappala, Emanuele |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Projection Methods for Operator Learning and Universal Approximation
by: Zappala, Emanuele
Published: (2024)
by: Zappala, Emanuele
Published: (2024)
Universal Approximation of Operators with Transformers and Neural Integral Operators
by: Zappala, Emanuele, et al.
Published: (2024)
by: Zappala, Emanuele, et al.
Published: (2024)
Spectral methods for Neural Integral Equations
by: Zappala, Emanuele
Published: (2023)
by: Zappala, Emanuele
Published: (2023)
Neural Operator: Learning Maps Between Function Spaces
by: Kovachki, Nikola, et al.
Published: (2021)
by: Kovachki, Nikola, et al.
Published: (2021)
Learning Operators through Coefficient Mappings in Fixed Basis Spaces
by: Chen, Chuqi, et al.
Published: (2025)
by: Chen, Chuqi, et al.
Published: (2025)
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
by: Lanthaler, Samuel
Published: (2024)
by: Lanthaler, Samuel
Published: (2024)
MODNO: Multi Operator Learning With Distributed Neural Operators
by: Zhang, Zecheng
Published: (2024)
by: Zhang, Zecheng
Published: (2024)
Operator Learning: Algorithms and Analysis
by: Kovachki, Nikola B., et al.
Published: (2024)
by: Kovachki, Nikola B., et al.
Published: (2024)
The Parametric Complexity of Operator Learning
by: Lanthaler, Samuel, et al.
Published: (2023)
by: Lanthaler, Samuel, et al.
Published: (2023)
Operator Learning at Machine Precision
by: Bacho, Aras, et al.
Published: (2025)
by: Bacho, Aras, et al.
Published: (2025)
UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
by: Qiao, Hanli, et al.
Published: (2026)
by: Qiao, Hanli, et al.
Published: (2026)
Data Complexity Estimates for Operator Learning
by: Kovachki, Nikola B., et al.
Published: (2024)
by: Kovachki, Nikola B., et al.
Published: (2024)
Learning the Hodgkin-Huxley Model with Operator Learning Techniques
by: Centofanti, Edoardo, et al.
Published: (2024)
by: Centofanti, Edoardo, et al.
Published: (2024)
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
by: Lowery, Matthew, et al.
Published: (2024)
by: Lowery, Matthew, et al.
Published: (2024)
Nonlocality and Nonlinearity Implies Universality in Operator Learning
by: Lanthaler, Samuel, et al.
Published: (2023)
by: Lanthaler, Samuel, et al.
Published: (2023)
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
by: Cole, Frank, et al.
Published: (2024)
by: Cole, Frank, et al.
Published: (2024)
Cauchy Random Features for Operator Learning in Sobolev Space
by: Liao, Chunyang, et al.
Published: (2025)
by: Liao, Chunyang, et al.
Published: (2025)
A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
by: Weihs, Adrien, et al.
Published: (2025)
by: Weihs, Adrien, et al.
Published: (2025)
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)
by: Li, Zongyi, et al.
Published: (2022)
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
by: Subedi, Unique, et al.
Published: (2024)
by: Subedi, Unique, et al.
Published: (2024)
An Efficient Deep Learning Approach for Approximating Parameter-to-Solution Maps of PDEs
by: Lei, Guanhang, et al.
Published: (2024)
by: Lei, Guanhang, et al.
Published: (2024)
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)
by: Feng, Xue, et al.
Published: (2026)
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
by: Pellegrini, Luca, et al.
Published: (2025)
by: Pellegrini, Luca, et al.
Published: (2025)
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
by: Gosea, Ion Victor, et al.
Published: (2024)
by: Gosea, Ion Victor, et al.
Published: (2024)
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
by: Chen, Junfeng, et al.
Published: (2024)
by: Chen, Junfeng, et al.
Published: (2024)
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
by: Sun, Jingmin, et al.
Published: (2024)
by: Sun, Jingmin, et al.
Published: (2024)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)
by: Gonon, Lukas, et al.
Published: (2024)
Stabilizing and Solving Unique Continuation Problems by Parameterizing Data and Learning Finite Element Solution Operators
by: Burman, Erik, et al.
Published: (2024)
by: Burman, Erik, et al.
Published: (2024)
DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving
by: Yue, Xihang, et al.
Published: (2024)
by: Yue, Xihang, et al.
Published: (2024)
Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
by: Wang, Yueqi, et al.
Published: (2025)
by: Wang, Yueqi, et al.
Published: (2025)
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
by: Jalalian, Yasamin, et al.
Published: (2025)
by: Jalalian, Yasamin, et al.
Published: (2025)
Continuum Attention for Neural Operators
by: Calvello, Edoardo, et al.
Published: (2024)
by: Calvello, Edoardo, et al.
Published: (2024)
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)
by: Ma, Qinglong, et al.
Published: (2024)
SpectraKAN: Conditioning Spectral Operators
by: Cheng, Chun-Wun, et al.
Published: (2026)
by: Cheng, Chun-Wun, et al.
Published: (2026)
Spectral Audit of In-Context Operator Networks
by: Gao, Zhiwei, et al.
Published: (2026)
by: Gao, Zhiwei, et al.
Published: (2026)
Operator Learning for Smoothing and Forecasting
by: Calvello, Edoardo, et al.
Published: (2026)
by: Calvello, Edoardo, et al.
Published: (2026)
Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^2$-Regularized Nonlinear Networks
by: Zangrando, Emanuele, et al.
Published: (2024)
by: Zangrando, Emanuele, et al.
Published: (2024)
A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
by: Ling, Shuo, et al.
Published: (2024)
by: Ling, Shuo, et al.
Published: (2024)
Geometry-aware training of factorized layers in tensor Tucker format
by: Zangrando, Emanuele, et al.
Published: (2023)
by: Zangrando, Emanuele, et al.
Published: (2023)
Similar Items
-
Projection Methods for Operator Learning and Universal Approximation
by: Zappala, Emanuele
Published: (2024) -
Universal Approximation of Operators with Transformers and Neural Integral Operators
by: Zappala, Emanuele, et al.
Published: (2024) -
Spectral methods for Neural Integral Equations
by: Zappala, Emanuele
Published: (2023) -
Neural Operator: Learning Maps Between Function Spaces
by: Kovachki, Nikola, et al.
Published: (2021) -
Learning Operators through Coefficient Mappings in Fixed Basis Spaces
by: Chen, Chuqi, et al.
Published: (2025)