High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
Fuente:
arXiv
Saved in:
| Main Authors: | Yuan, Biao, Wang, He, Song, Yanjie, Heitor, Ana, Chen, Xiaohui |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
by: Pellegrini, Luca, et al.
Published: (2025)
by: Pellegrini, Luca, et al.
Published: (2025)
HyperNOs: Automated and Parallel Library for Neural Operators Research
by: Ghiotto, Massimiliano
Published: (2025)
by: Ghiotto, Massimiliano
Published: (2025)
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)
Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
by: Li, Xinyu, et al.
Published: (2026)
by: Li, Xinyu, et al.
Published: (2026)
Physics-informed Discretization-independent Deep Compositional Operator Network
by: Zhong, Weiheng, et al.
Published: (2024)
by: Zhong, Weiheng, et al.
Published: (2024)
Physics-Informed Geometry-Aware Neural Operator
by: Zhong, Weiheng, et al.
Published: (2024)
by: Zhong, Weiheng, et al.
Published: (2024)
Deep NURBS -- Admissible Physics-informed Neural Networks
by: Saidaoui, Hamed, et al.
Published: (2022)
by: Saidaoui, Hamed, et al.
Published: (2022)
Neural Conjugate Flows: Physics-informed architectures with flow structure
by: Bizzi, Arthur, et al.
Published: (2024)
by: Bizzi, Arthur, et al.
Published: (2024)
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
by: Liu, Chaoyu, et al.
Published: (2023)
by: Liu, Chaoyu, et al.
Published: (2023)
Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
by: Kim, Taeyoung, et al.
Published: (2024)
by: Kim, Taeyoung, et al.
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)
Parallel-in-Time Solutions with Random Projection Neural Networks
by: Betcke, Marta M., et al.
Published: (2024)
by: Betcke, Marta M., et al.
Published: (2024)
Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model
by: Pellegrini, Luca
Published: (2026)
by: Pellegrini, Luca
Published: (2026)
Continuum Attention for Neural Operators
by: Calvello, Edoardo, et al.
Published: (2024)
by: Calvello, Edoardo, et al.
Published: (2024)
WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
by: Zhu, Bokai, et al.
Published: (2026)
by: Zhu, Bokai, et al.
Published: (2026)
Parareal Neural Networks Emulating a Parallel-in-time Algorithm
by: Lee, Chang-Ock, et al.
Published: (2021)
by: Lee, Chang-Ock, et al.
Published: (2021)
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
by: Yang, Yu, et al.
Published: (2023)
by: Yang, Yu, et al.
Published: (2023)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)
by: Katende, Ronald
Published: (2025)
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)
Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions
by: Wang, Jihong, et al.
Published: (2025)
by: Wang, Jihong, et al.
Published: (2025)
A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
by: Chen, Zhenglong, et al.
Published: (2025)
by: Chen, Zhenglong, et al.
Published: (2025)
Diffeomorphism Neural Operator for various domains and parameters of partial differential equations
by: Zhao, Zhiwei, et al.
Published: (2024)
by: Zhao, Zhiwei, et al.
Published: (2024)
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
by: Cheng, Nuojin, et al.
Published: (2023)
by: Cheng, Nuojin, et al.
Published: (2023)
A Mathematical Analysis of Neural Operator Behaviors
by: Le, Vu-Anh, et al.
Published: (2024)
by: Le, Vu-Anh, et al.
Published: (2024)
Quasi-Random Physics-informed Neural Networks
by: Yu, Tianchi, et al.
Published: (2025)
by: Yu, Tianchi, et al.
Published: (2025)
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
by: Park, Youngjae, et al.
Published: (2026)
by: Park, Youngjae, et al.
Published: (2026)
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)
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
by: Yu, Yifan, et al.
Published: (2025)
by: Yu, Yifan, et al.
Published: (2025)
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)
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)
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
by: Katende, Ronald
Published: (2025)
by: Katende, Ronald
Published: (2025)
Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics
by: Santos, Rômulo Damasclin Chaves dos, et al.
Published: (2025)
by: Santos, Rômulo Damasclin Chaves dos, et al.
Published: (2025)
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
by: Wang, Sifan, et al.
Published: (2024)
by: Wang, Sifan, et al.
Published: (2024)
Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
by: Alegría, Samuel A. Cruz, et al.
Published: (2025)
by: Alegría, Samuel A. Cruz, et al.
Published: (2025)
Multi-Level Monte Carlo Training of Neural Operators
by: Rowbottom, James, et al.
Published: (2025)
by: Rowbottom, James, et al.
Published: (2025)
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)
by: Furuya, Takashi, et al.
Published: (2024)
Revisiting Orbital Minimization Method for Neural Operator Decomposition
by: Ryu, J. Jon, et al.
Published: (2025)
by: Ryu, J. Jon, et al.
Published: (2025)
Neural Operator: Learning Maps Between Function Spaces
by: Kovachki, Nikola, et al.
Published: (2021)
by: Kovachki, Nikola, et al.
Published: (2021)
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
by: Tu, Renbo, et al.
Published: (2023)
by: Tu, Renbo, et al.
Published: (2023)
Similar Items
-
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
by: Pellegrini, Luca, et al.
Published: (2025) -
HyperNOs: Automated and Parallel Library for Neural Operators Research
by: Ghiotto, Massimiliano
Published: (2025) -
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024) -
Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
by: Li, Xinyu, et al.
Published: (2026) -
Physics-informed Discretization-independent Deep Compositional Operator Network
by: Zhong, Weiheng, et al.
Published: (2024)