LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Jing, Chen, Biao, Xie, Hairun, Wang, Rui, Xia, Yifan, Zhang, Jifa, Xu, Hui |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026)
by: Chen, Biao, et al.
Published: (2026)
Physics-Informed Neural Networks and Neural Operators for Parametric PDEs
by: Zhang, Zhuo, et al.
Published: (2025)
by: Zhang, Zhuo, et al.
Published: (2025)
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
by: Ganeshram, Adarsh, et al.
Published: (2022)
by: Ganeshram, Adarsh, et al.
Published: (2022)
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)
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)
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
by: Lai, Pengyu, et al.
Published: (2025)
by: Lai, Pengyu, et al.
Published: (2025)
Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
by: Zhao, Xuanle, et al.
Published: (2023)
by: Zhao, Xuanle, et al.
Published: (2023)
HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
by: Bischof, Rafael, et al.
Published: (2025)
by: Bischof, Rafael, et al.
Published: (2025)
Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEs
by: Karumuri, Sharmila, et al.
Published: (2025)
by: Karumuri, Sharmila, et al.
Published: (2025)
Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
by: Li, Shanda, et al.
Published: (2025)
by: Li, Shanda, et al.
Published: (2025)
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
by: Rezaei, Shahed, et al.
Published: (2024)
by: Rezaei, Shahed, et al.
Published: (2024)
Fourier Feature Pyramids for Physics-Informed Neural Networks
by: Zhao, Brandon, et al.
Published: (2026)
by: Zhao, Brandon, et al.
Published: (2026)
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
by: Wang, Tian, et al.
Published: (2024)
by: Wang, Tian, et al.
Published: (2024)
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometries
by: Asl, Reza Najian, et al.
Published: (2025)
by: Asl, Reza Najian, et al.
Published: (2025)
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
by: Wang, Haixin, et al.
Published: (2024)
by: Wang, Haixin, et al.
Published: (2024)
Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator
by: Sun, Yifan, et al.
Published: (2025)
by: Sun, Yifan, et al.
Published: (2025)
General Fourier Feature Physics-Informed Extreme Learning Machine (GFF-PIELM) for High-Frequency PDEs
by: Ren, Fei, et al.
Published: (2025)
by: Ren, Fei, et al.
Published: (2025)
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
by: Dai, Yilong, et al.
Published: (2026)
by: Dai, Yilong, et al.
Published: (2026)
Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics
by: Qin, Jing, et al.
Published: (2026)
by: Qin, Jing, et al.
Published: (2026)
Sumudu Neural Operator for ODEs and PDEs
by: Zelenskiy, Ben, et al.
Published: (2025)
by: Zelenskiy, Ben, et al.
Published: (2025)
Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts
by: Xu, Jiaxiao, et al.
Published: (2026)
by: Xu, Jiaxiao, et al.
Published: (2026)
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
by: Hoshisashi, Kentaro, et al.
Published: (2026)
by: Hoshisashi, Kentaro, et al.
Published: (2026)
Graph Fourier Neural Kernels (G-FuNK): Learning Solutions of Nonlinear Diffusive Parametric PDEs on Multiple Domains
by: Loeffler, Shane E., et al.
Published: (2024)
by: Loeffler, Shane E., et al.
Published: (2024)
Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
by: Dong, Guodan, et al.
Published: (2026)
by: Dong, Guodan, et al.
Published: (2026)
Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
by: Xiong, Xiong, et al.
Published: (2025)
by: Xiong, Xiong, et al.
Published: (2025)
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
by: Behroozi, Abdolmehdi, et al.
Published: (2025)
by: Behroozi, Abdolmehdi, et al.
Published: (2025)
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
by: Liu, Ning, et al.
Published: (2025)
by: Liu, Ning, et al.
Published: (2025)
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)
by: Lei, Guanhang, et al.
Published: (2023)
Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
by: Li, Zhihao, et al.
Published: (2024)
by: Li, Zhihao, et al.
Published: (2024)
Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
by: Serrano, Louis, et al.
Published: (2024)
by: Serrano, Louis, et al.
Published: (2024)
From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs
by: Lai, Pengyu, et al.
Published: (2026)
by: Lai, Pengyu, et al.
Published: (2026)
Backstepping Neural Operators for $2\times 2$ Hyperbolic PDEs
by: Wang, Shanshan, et al.
Published: (2023)
by: Wang, Shanshan, et al.
Published: (2023)
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
by: Farea, Afrah, et al.
Published: (2025)
by: Farea, Afrah, et al.
Published: (2025)
DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
by: Luo, Dongyu, et al.
Published: (2024)
by: Luo, Dongyu, et al.
Published: (2024)
Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries
by: Zeng, Chenyu, et al.
Published: (2025)
by: Zeng, Chenyu, et al.
Published: (2025)
Isotropic Fourier Neural Operators
by: Staddon, Michael F.
Published: (2026)
by: Staddon, Michael F.
Published: (2026)
Replay-Based Continual Learning for Physics-Informed Neural Operators
by: Wang, Yizheng, et al.
Published: (2026)
by: Wang, Yizheng, et al.
Published: (2026)
Generalized Lie Symmetries in Physics-Informed Neural Operators
by: Wang, Amy Xiang, et al.
Published: (2025)
by: Wang, Amy Xiang, et al.
Published: (2025)
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
by: Lee, Youngkyu, et al.
Published: (2025)
by: Lee, Youngkyu, et al.
Published: (2025)
MgFNO: Multi-grid Architecture Fourier Neural Operator for Parametric Partial Differential Equations
by: Guo, Zi-Hao, et al.
Published: (2024)
by: Guo, Zi-Hao, et al.
Published: (2024)
Similar Items
-
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026) -
Physics-Informed Neural Networks and Neural Operators for Parametric PDEs
by: Zhang, Zhuo, et al.
Published: (2025) -
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
by: Ganeshram, Adarsh, et al.
Published: (2022) -
Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
by: Wang, Yueqi, et al.
Published: (2025) -
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)