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Operator learning with PCA-Net: upper and lower complexity bounds
v1v2v3v4v5 (latest)

Operator learning with PCA-Net: upper and lower complexity bounds

28 March 2023
S. Lanthaler
ArXiv (abs)PDFHTML

Papers citing "Operator learning with PCA-Net: upper and lower complexity bounds"

19 / 19 papers shown
Title
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Julius Berner
Miguel Liu-Schiaffini
Jean Kossaifi
Valentin Duruisseaux
Boris Bonev
Kamyar Azizzadenesheli
A. Anandkumar
AI4CE
127
0
0
12 Jun 2025
Learning Dual-Arm Coordination for Grasping Large Flat Objects
Learning Dual-Arm Coordination for Grasping Large Flat Objects
Yongliang Wang
Hamidreza Kasaei
99
2
0
04 Apr 2025
Theory-to-Practice Gap for Neural Networks and Neural Operators
Theory-to-Practice Gap for Neural Networks and Neural Operators
Philipp Grohs
S. Lanthaler
Margaret Trautner
117
3
0
23 Mar 2025
Neural Operators Can Play Dynamic Stackelberg Games
Neural Operators Can Play Dynamic Stackelberg Games
Guillermo Alvarez
Ibrahim Ekren
Anastasis Kratsios
Xuwei Yang
68
0
0
14 Nov 2024
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators
Shao-Ting Chiu
Junyuan Hong
Ulisses Braga-Neto
BDL
71
1
0
11 Oct 2024
Quantitative Approximation for Neural Operators in Nonlinear Parabolic
  Equations
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
Takashi Furuya
K. Taniguchi
Satoshi Okuda
80
0
0
03 Oct 2024
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A
  Theoretical Study
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
Hao Liu
Zecheng Zhang
Wenjing Liao
Hayden Schaeffer
73
1
0
01 Oct 2024
Operator Learning Using Random Features: A Tool for Scientific Computing
Operator Learning Using Random Features: A Tool for Scientific Computing
Nicholas H. Nelsen
Andrew M. Stuart
101
14
0
12 Aug 2024
Operator Learning of Lipschitz Operators: An Information-Theoretic
  Perspective
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
Samuel Lanthaler
95
4
0
26 Jun 2024
Mixture of Experts Soften the Curse of Dimensionality in Operator
  Learning
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning
Anastasis Kratsios
Takashi Furuya
Jose Antonio Lara Benitez
Matti Lassas
Maarten V. de Hoop
90
14
0
13 Apr 2024
Learning smooth functions in high dimensions: from sparse polynomials to
  deep neural networks
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
61
4
0
04 Apr 2024
Operator Learning: Algorithms and Analysis
Operator Learning: Algorithms and Analysis
Nikola B. Kovachki
S. Lanthaler
Andrew M. Stuart
148
33
0
24 Feb 2024
A practical existence theorem for reduced order models based on
  convolutional autoencoders
A practical existence theorem for reduced order models based on convolutional autoencoders
N. R. Franco
Simone Brugiapaglia
AI4CE
83
4
0
01 Feb 2024
Online Infinite-Dimensional Regression: Learning Linear Operators
Online Infinite-Dimensional Regression: Learning Linear Operators
Vinod Raman
Unique Subedi
Ambuj Tewari
51
0
0
08 Sep 2023
Deep Operator Network Approximation Rates for Lipschitz Operators
Deep Operator Network Approximation Rates for Lipschitz Operators
Ch. Schwab
A. Stein
Jakob Zech
60
10
0
19 Jul 2023
Variational operator learning: A unified paradigm marrying training
  neural operators and solving partial differential equations
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
Tengfei Xu
Dachuan Liu
Peng Hao
Bo Wang
101
7
0
09 Apr 2023
Deep Learning Methods for Partial Differential Equations and Related
  Parameter Identification Problems
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
Derick Nganyu Tanyu
Jianfeng Ning
Tom Freudenberg
Nick Heilenkötter
A. Rademacher
U. Iben
Peter Maass
AI4CE
92
35
0
06 Dec 2022
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Luca Galimberti
Anastasis Kratsios
Giulia Livieri
OOD
123
14
0
24 Oct 2022
Neural and spectral operator surrogates: unified construction and
  expression rate bounds
Neural and spectral operator surrogates: unified construction and expression rate bounds
L. Herrmann
Christoph Schwab
Jakob Zech
101
11
0
11 Jul 2022
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