LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Jiahao, Wang, Yueqi, Lin, Guang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A POD-DeepONet Framework for Forward and Inverse Design of 2D Photonic Crystals
by: Wang, Yueqi, et al.
Published: (2026)
by: Wang, Yueqi, 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)
Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition
by: Mou, Changhong, et al.
Published: (2026)
by: Mou, Changhong, et al.
Published: (2026)
Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems
by: Lin, Guang, et al.
Published: (2025)
by: Lin, Guang, et al.
Published: (2025)
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)
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)
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
by: Jiang, Zhaoxi, et al.
Published: (2025)
by: Jiang, Zhaoxi, et al.
Published: (2025)
Kernel Learning of PDE Solution Operators
by: Hu, Jianyu, et al.
Published: (2026)
by: Hu, Jianyu, et al.
Published: (2026)
Plug and Play Splitting Techniques for Poisson Image Restoration
by: Benfenati, Alessandro
Published: (2025)
by: Benfenati, Alessandro
Published: (2025)
Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
by: Lee, Jae Yong, et al.
Published: (2024)
by: Lee, Jae Yong, et al.
Published: (2024)
A PDE Perspective on Approximating Nonlocal Periodic Operators with Applications on Neural Networks for Critical SQG Equations
by: Abdo, Elie, et al.
Published: (2024)
by: Abdo, Elie, et al.
Published: (2024)
Latent Neural Operator for Solving Forward and Inverse PDE Problems
by: Wang, Tian, et al.
Published: (2024)
by: Wang, Tian, et al.
Published: (2024)
R-adaptive DeepONet: Learning Solution Operators for PDEs with Discontinuous Solutions Using an R-adaptive Strategy
by: Zhu, Yameng, et al.
Published: (2024)
by: Zhu, Yameng, et al.
Published: (2024)
DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems
by: Liu, Haibo, et al.
Published: (2026)
by: Liu, Haibo, et al.
Published: (2026)
Locally Subspace-Informed Neural Operators for Efficient Multiscale PDE Solving
by: Rudikov, Alexander, et al.
Published: (2025)
by: Rudikov, Alexander, et al.
Published: (2025)
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
by: Zhang, Enrui, et al.
Published: (2022)
by: Zhang, Enrui, et al.
Published: (2022)
The Calderón's problem via DeepONets
by: Castro, Javier, et al.
Published: (2022)
by: Castro, Javier, et al.
Published: (2022)
DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios
by: Yang, Bo, et al.
Published: (2025)
by: Yang, Bo, et al.
Published: (2025)
CATO: Charted Attention for Neural PDE Operators
by: Cheng, Chun-Wun, et al.
Published: (2026)
by: Cheng, Chun-Wun, et al.
Published: (2026)
Neural Parameter Regression for Explicit Representations of PDE Solution Operators
by: Mundinger, Konrad, et al.
Published: (2024)
by: Mundinger, Konrad, et al.
Published: (2024)
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)
by: Bryutkin, Andrey, et al.
Published: (2024)
Fine-Tuning DeepONets to Enhance Physics-informed Neural Networks for solving Partial Differential Equations
by: Wu, Sidi
Published: (2024)
by: Wu, Sidi
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)
Finite Element Representation Network (FERN) for Operator Learning with a Localized Trainable Basis
by: Zhang, Zecheng, et al.
Published: (2025)
by: Zhang, Zecheng, et al.
Published: (2025)
Nodal Hybrid Neural Solvers for Parametric PDE Systems
by: Liu, Yun, et al.
Published: (2025)
by: Liu, Yun, et al.
Published: (2025)
Predicting band structures for 2D Photonic Crystals via Deep Learning
by: Wang, Yueqi, et al.
Published: (2024)
by: Wang, Yueqi, et al.
Published: (2024)
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
by: Cheng, Chun-Wun, et al.
Published: (2024)
by: Cheng, Chun-Wun, et al.
Published: (2024)
XI-DeepONet: An operator learning method for elliptic interface problems
by: Bi, Ran, et al.
Published: (2024)
by: Bi, Ran, et al.
Published: (2024)
Structure-Preserving Operator Learning
by: Bouziani, Nacime, et al.
Published: (2024)
by: Bouziani, Nacime, et al.
Published: (2024)
On a Modified Random Genetic Drift Model: Derivation and a Structure-Preserving Operator-Splitting Discretization
by: Chen, Chi-An, et al.
Published: (2025)
by: Chen, Chi-An, et al.
Published: (2025)
A Non-compact Positivity-Preserving Scheme for Parabolic PDE via Conditional Expectation
by: Xu, Haoran, et al.
Published: (2026)
by: Xu, Haoran, et al.
Published: (2026)
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)
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
by: Son, Hwijae
Published: (2025)
by: Son, Hwijae
Published: (2025)
Diffeomorphic Neural Operator Learning
by: Taylor, Seth, et al.
Published: (2025)
by: Taylor, Seth, et al.
Published: (2025)
Structure-Preserving Neural Ordinary Differential Equations for Stiff Systems
by: Loya, Allen Alvarez, et al.
Published: (2025)
by: Loya, Allen Alvarez, et al.
Published: (2025)
A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction
by: Gong, Yi, et al.
Published: (2025)
by: Gong, Yi, et al.
Published: (2025)
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)
by: Zheng, Haoyang, et al.
Published: (2025)
Learning Neural PDE Solvers with Convergence Guarantees
by: Hsieh, Jun-Ting, et al.
Published: (2019)
by: Hsieh, Jun-Ting, et al.
Published: (2019)
MODNO: Multi Operator Learning With Distributed Neural Operators
by: Zhang, Zecheng
Published: (2024)
by: Zhang, Zecheng
Published: (2024)
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)
by: Zhang, Rui, et al.
Published: (2023)
Similar Items
-
A POD-DeepONet Framework for Forward and Inverse Design of 2D Photonic Crystals
by: Wang, Yueqi, 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) -
Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition
by: Mou, Changhong, et al.
Published: (2026) -
Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems
by: Lin, Guang, et al.
Published: (2025) -
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
by: Moya, Christian, et al.
Published: (2024)