Towards Size-Independent Generalization Bounds for Deep Operator Nets
Fuente:
arXiv
Saved in:
| Main Authors: | Gopalani, Pulkit, Karmakar, Sayar, Kumar, Dibyakanti, Mukherjee, Anirbit |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
by: Kumar, Dibyakanti, et al.
Published: (2023)
by: Kumar, Dibyakanti, et al.
Published: (2023)
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
Size Lowerbounds for Deep Operator Networks
by: Mukherjee, Anirbit, et al.
Published: (2023)
by: Mukherjee, Anirbit, et al.
Published: (2023)
Global Convergence of SGD On Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2022)
by: Gopalani, Pulkit, et al.
Published: (2022)
Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
by: Kumar, Dibyakanti, et al.
Published: (2025)
by: Kumar, Dibyakanti, et al.
Published: (2025)
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2023)
by: Gopalani, Pulkit, et al.
Published: (2023)
Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
by: Ren, Mohan, et al.
Published: (2024)
by: Ren, Mohan, et al.
Published: (2024)
Convergent Stochastic Training of Attention and Understanding LoRA
by: Sun, Zhengkai, et al.
Published: (2026)
by: Sun, Zhengkai, et al.
Published: (2026)
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)
Towards Sharp Minimax Risk Bounds for Operator Learning
by: Adcock, Ben, et al.
Published: (2025)
by: Adcock, Ben, et al.
Published: (2025)
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
by: Tu, Renbo, et al.
Published: (2023)
by: Tu, Renbo, et al.
Published: (2023)
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
by: Wang, Sifan, et al.
Published: (2024)
by: Wang, Sifan, et al.
Published: (2024)
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
by: Moya, Christian, et al.
Published: (2024)
by: Moya, Christian, et al.
Published: (2024)
Physics-informed Discretization-independent Deep Compositional Operator Network
by: Zhong, Weiheng, et al.
Published: (2024)
by: Zhong, Weiheng, et al.
Published: (2024)
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)
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
by: Wei, Zhi-Feng, et al.
Published: (2025)
by: Wei, Zhi-Feng, et al.
Published: (2025)
Solving PDEs With Deep Neural Nets under General Boundary Conditions
by: Zhang, Chenggong
Published: (2025)
by: Zhang, Chenggong
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)
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification
by: Penka, Maximilian
Published: (2024)
by: Penka, Maximilian
Published: (2024)
Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions
by: Wang, Bo, et al.
Published: (2025)
by: Wang, Bo, et al.
Published: (2025)
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
by: Chen, Zhuo, et al.
Published: (2024)
by: Chen, Zhuo, 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)
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations
by: Tang, Shanshan, et al.
Published: (2019)
by: Tang, Shanshan, et al.
Published: (2019)
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)
DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
by: Huang, Chih-Kang, et al.
Published: (2026)
by: Huang, Chih-Kang, et al.
Published: (2026)
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)
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)
Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
by: Mahowald, Jamie, et al.
Published: (2026)
by: Mahowald, Jamie, et al.
Published: (2026)
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)
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
by: Lanthaler, Samuel
Published: (2024)
by: Lanthaler, Samuel
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)
MODNO: Multi Operator Learning With Distributed Neural Operators
by: Zhang, Zecheng
Published: (2024)
by: Zhang, Zecheng
Published: (2024)
ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
by: Zheng, Haolan, et al.
Published: (2025)
by: Zheng, Haolan, et al.
Published: (2025)
fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
by: Lu, Binghang, et al.
Published: (2026)
by: Lu, Binghang, et al.
Published: (2026)
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)
Step-Size Decay and Structural Stagnation in Greedy Sparse Learning
by: Berná, Pablo M.
Published: (2026)
by: Berná, Pablo M.
Published: (2026)
Continuum Attention for Neural Operators
by: Calvello, Edoardo, et al.
Published: (2024)
by: Calvello, Edoardo, 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: Algorithms and Analysis
by: Kovachki, Nikola B., et al.
Published: (2024)
by: Kovachki, Nikola B., et al.
Published: (2024)
Operator Learning at Machine Precision
by: Bacho, Aras, et al.
Published: (2025)
by: Bacho, Aras, et al.
Published: (2025)
Similar Items
-
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
by: Kumar, Dibyakanti, et al.
Published: (2023) -
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026) -
Size Lowerbounds for Deep Operator Networks
by: Mukherjee, Anirbit, et al.
Published: (2023) -
Global Convergence of SGD On Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2022) -
Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
by: Kumar, Dibyakanti, et al.
Published: (2025)