One-shot learning for solution operators of partial differential equations
Fuente:
arXiv
Salvato in:
| Autori principali: | Jiao, Anran, He, Haiyang, Ranade, Rishikesh, Pathak, Jay, Lu, Lu |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2021
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
Documenti analoghi
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
di: Nidhan, Sheel, et al.
Pubblicazione: (2024)
di: Nidhan, Sheel, et al.
Pubblicazione: (2024)
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
di: Lee, Youngkyu, et al.
Pubblicazione: (2024)
di: Lee, Youngkyu, et al.
Pubblicazione: (2024)
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
di: Liu, Xin-Yang, et al.
Pubblicazione: (2022)
di: Liu, Xin-Yang, et al.
Pubblicazione: (2022)
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
di: Adams, Corey, et al.
Pubblicazione: (2025)
di: Adams, Corey, et al.
Pubblicazione: (2025)
Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
di: Jiao, Anran, et al.
Pubblicazione: (2026)
di: Jiao, Anran, et al.
Pubblicazione: (2026)
X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation
di: Nabian, Mohammad Amin, et al.
Pubblicazione: (2024)
di: Nabian, Mohammad Amin, et al.
Pubblicazione: (2024)
PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations
di: Zhu, Min, et al.
Pubblicazione: (2025)
di: Zhu, Min, et al.
Pubblicazione: (2025)
Sampling-based Distributed Training with Message Passing Neural Network
di: Kakka, Priyesh, et al.
Pubblicazione: (2024)
di: Kakka, Priyesh, et al.
Pubblicazione: (2024)
RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
di: Ouyang, Weihang, et al.
Pubblicazione: (2025)
di: Ouyang, Weihang, et al.
Pubblicazione: (2025)
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
di: Zhang, Handi, et al.
Pubblicazione: (2024)
di: Zhang, Handi, et al.
Pubblicazione: (2024)
Local neural operator for solving transient partial differential equations on varied domains
di: Li, Hongyu, et al.
Pubblicazione: (2022)
di: Li, Hongyu, et al.
Pubblicazione: (2022)
Active operator learning with predictive uncertainty quantification for partial differential equations
di: Winovich, Nick, et al.
Pubblicazione: (2025)
di: Winovich, Nick, et al.
Pubblicazione: (2025)
Distributed physics informed neural network for data-efficient solution to partial differential equations
di: Dwivedi, Vikas, et al.
Pubblicazione: (2019)
di: Dwivedi, Vikas, et al.
Pubblicazione: (2019)
PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations
di: Song, Jin, et al.
Pubblicazione: (2025)
di: Song, Jin, et al.
Pubblicazione: (2025)
DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations
di: Ranade, Rishikesh, et al.
Pubblicazione: (2025)
di: Ranade, Rishikesh, et al.
Pubblicazione: (2025)
Automotive Crash Dynamics Modeling Accelerated with Machine Learning
di: Nabian, Mohammad Amin, et al.
Pubblicazione: (2025)
di: Nabian, Mohammad Amin, et al.
Pubblicazione: (2025)
Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
di: Monsel, Thibault, et al.
Pubblicazione: (2024)
di: Monsel, Thibault, et al.
Pubblicazione: (2024)
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?
di: Zhou, Zijian, et al.
Pubblicazione: (2024)
di: Zhou, Zijian, et al.
Pubblicazione: (2024)
Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
di: Sukumar, N., et al.
Pubblicazione: (2024)
di: Sukumar, N., et al.
Pubblicazione: (2024)
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
di: Rezaei, Shahed, et al.
Pubblicazione: (2024)
di: Rezaei, Shahed, et al.
Pubblicazione: (2024)
Discretization-independent multifidelity operator learning for partial differential equations
di: Hauck, Jacob, et al.
Pubblicazione: (2025)
di: Hauck, Jacob, et al.
Pubblicazione: (2025)
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
di: Zhang, Enrui, et al.
Pubblicazione: (2022)
di: Zhang, Enrui, et al.
Pubblicazione: (2022)
One-shot acceleration of transient PDE solvers via online-learned preconditioners
di: Khodak, Mikhail, et al.
Pubblicazione: (2025)
di: Khodak, Mikhail, et al.
Pubblicazione: (2025)
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
di: Jacob, Bruno, et al.
Pubblicazione: (2025)
di: Jacob, Bruno, et al.
Pubblicazione: (2025)
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
di: Shukla, Khemraj, et al.
Pubblicazione: (2024)
di: Shukla, Khemraj, et al.
Pubblicazione: (2024)
Koopman neural operator as a mesh-free solver of non-linear partial differential equations
di: Xiong, Wei, et al.
Pubblicazione: (2023)
di: Xiong, Wei, et al.
Pubblicazione: (2023)
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems
di: Pal, Ashish, et al.
Pubblicazione: (2024)
di: Pal, Ashish, et al.
Pubblicazione: (2024)
AI paradigm for solving differential equations: first-principles data generation and scale-dilation operator AI solver
di: Gong, Xiangshu, et al.
Pubblicazione: (2025)
di: Gong, Xiangshu, et al.
Pubblicazione: (2025)
Breakeven complexity: A new perspective on neural partial differential equation solvers
di: Zhang, Yijing, et al.
Pubblicazione: (2026)
di: Zhang, Yijing, et al.
Pubblicazione: (2026)
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration
di: Jiang, Zhongyi, et al.
Pubblicazione: (2023)
di: Jiang, Zhongyi, et al.
Pubblicazione: (2023)
Single-shot prediction of parametric partial differential equations
di: Rafiq, Khalid, et al.
Pubblicazione: (2025)
di: Rafiq, Khalid, et al.
Pubblicazione: (2025)
Quantum DeepONet: Neural operators accelerated by quantum computing
di: Xiao, Pengpeng, et al.
Pubblicazione: (2024)
di: Xiao, Pengpeng, et al.
Pubblicazione: (2024)
DynGMA: a robust approach for learning stochastic differential equations from data
di: Zhu, Aiqing, et al.
Pubblicazione: (2024)
di: Zhu, Aiqing, et al.
Pubblicazione: (2024)
Equivariance and partial observations in Koopman operator theory for partial differential equations
di: Peitz, Sebastian, et al.
Pubblicazione: (2023)
di: Peitz, Sebastian, et al.
Pubblicazione: (2023)
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
di: Lu, Minglei, et al.
Pubblicazione: (2024)
di: Lu, Minglei, et al.
Pubblicazione: (2024)
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
di: Zhu, Min, et al.
Pubblicazione: (2023)
di: Zhu, Min, et al.
Pubblicazione: (2023)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
di: Xu, Tengfei, et al.
Pubblicazione: (2023)
di: Xu, Tengfei, et al.
Pubblicazione: (2023)
Learning collision operators from plasma phase space data using differentiable simulators
di: Carvalho, Diogo D., et al.
Pubblicazione: (2026)
di: Carvalho, Diogo D., et al.
Pubblicazione: (2026)
Reinforcement learning-based estimation for partial differential equations
di: Mowlavi, Saviz, et al.
Pubblicazione: (2023)
di: Mowlavi, Saviz, et al.
Pubblicazione: (2023)
Dilated convolution neural operator for multiscale partial differential equations
di: Xu, Bo, et al.
Pubblicazione: (2024)
di: Xu, Bo, et al.
Pubblicazione: (2024)
Documenti analoghi
-
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
di: Nidhan, Sheel, et al.
Pubblicazione: (2024) -
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
di: Lee, Youngkyu, et al.
Pubblicazione: (2024) -
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
di: Liu, Xin-Yang, et al.
Pubblicazione: (2022) -
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
di: Adams, Corey, et al.
Pubblicazione: (2025) -
Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
di: Jiao, Anran, et al.
Pubblicazione: (2026)