Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
Fuente:
arXiv
Guardado en:
| Autores principales: | Zeudong, Etienne, Cardoso-Bihlo, Elsa, Bihlo, Alex |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
por: Cardoso-Bihlo, Elsa, et al.
Publicado: (2026)
por: Cardoso-Bihlo, Elsa, et al.
Publicado: (2026)
Improving physics-informed DeepONets with hard constraints
por: Brecht, Rüdiger, et al.
Publicado: (2023)
por: Brecht, Rüdiger, et al.
Publicado: (2023)
Physics-informed neural networks for the shallow-water equations on the sphere
por: Bihlo, Alex, et al.
Publicado: (2021)
por: Bihlo, Alex, et al.
Publicado: (2021)
A shallow physics-informed neural network for solving partial differential equations on surfaces
por: Hu, Wei-Fan, et al.
Publicado: (2022)
por: Hu, Wei-Fan, et al.
Publicado: (2022)
Diffeomorphic Neural Operator Learning
por: Taylor, Seth, et al.
Publicado: (2025)
por: Taylor, Seth, et al.
Publicado: (2025)
Learning vertical coordinates via automatic differentiation of a dynamical core
por: Whittaker, Tim, et al.
Publicado: (2025)
por: Whittaker, Tim, et al.
Publicado: (2025)
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
por: Williams, Emily, et al.
Publicado: (2024)
por: Williams, Emily, et al.
Publicado: (2024)
Learning by solving differential equations
por: Dherin, Benoit, et al.
Publicado: (2025)
por: Dherin, Benoit, et al.
Publicado: (2025)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
por: Liu, Ye, et al.
Publicado: (2024)
por: Liu, Ye, et al.
Publicado: (2024)
DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios
por: Yang, Bo, et al.
Publicado: (2025)
por: Yang, Bo, et al.
Publicado: (2025)
Deep Neural networks for solving high-dimensional parabolic partial differential equations
por: Zhang, Wenzhong, et al.
Publicado: (2026)
por: Zhang, Wenzhong, et al.
Publicado: (2026)
Physics-informed neural networks for tsunami inundation modeling
por: Brecht, Rüdiger, et al.
Publicado: (2024)
por: Brecht, Rüdiger, et al.
Publicado: (2024)
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
por: Berman, Jules, et al.
Publicado: (2024)
por: Berman, Jules, et al.
Publicado: (2024)
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
por: Zheng, Haoyang, et al.
Publicado: (2023)
por: Zheng, Haoyang, et al.
Publicado: (2023)
DeepONet-accelerated Bayesian inversion for moving boundary problems
por: Iglesias, Marco A., et al.
Publicado: (2025)
por: Iglesias, Marco A., et al.
Publicado: (2025)
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
por: Moya, Christian, et al.
Publicado: (2024)
por: Moya, Christian, et al.
Publicado: (2024)
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
por: Yang, Yahong
Publicado: (2024)
por: Yang, Yahong
Publicado: (2024)
NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
por: Levrero-Florencio, Francesc, et al.
Publicado: (2026)
por: Levrero-Florencio, Francesc, et al.
Publicado: (2026)
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
por: Zhang, Huan, et al.
Publicado: (2024)
por: Zhang, Huan, et al.
Publicado: (2024)
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
por: Blechschmidt, Jan, et al.
Publicado: (2025)
por: Blechschmidt, Jan, et al.
Publicado: (2025)
Pseudo-differential-enhanced physics-informed neural networks
por: Gracyk, Andrew
Publicado: (2026)
por: Gracyk, Andrew
Publicado: (2026)
A randomized algorithm to solve reduced rank operator regression
por: Turri, Giacomo, et al.
Publicado: (2023)
por: Turri, Giacomo, et al.
Publicado: (2023)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
por: Xu, Tengfei, et al.
Publicado: (2023)
por: Xu, Tengfei, et al.
Publicado: (2023)
PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
por: Matthews, Jason, et al.
Publicado: (2024)
por: Matthews, Jason, et al.
Publicado: (2024)
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
por: Thanasutives, Pongpisit, et al.
Publicado: (2024)
por: Thanasutives, Pongpisit, et al.
Publicado: (2024)
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
por: Jiang, Zhaoxi, et al.
Publicado: (2025)
por: Jiang, Zhaoxi, et al.
Publicado: (2025)
A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
por: Han, Jiequn, et al.
Publicado: (2025)
por: Han, Jiequn, et al.
Publicado: (2025)
ForeCite: Adapting Pre-Trained Language Models to Predict Future Citation Rates of Academic Papers
por: Hull, Gavin, et al.
Publicado: (2025)
por: Hull, Gavin, et al.
Publicado: (2025)
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
por: Son, Hwijae
Publicado: (2025)
por: Son, Hwijae
Publicado: (2025)
fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
por: Lu, Binghang, et al.
Publicado: (2026)
por: Lu, Binghang, et al.
Publicado: (2026)
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
por: Nguyen, Thi Nguyen Khoa, et al.
Publicado: (2022)
por: Nguyen, Thi Nguyen Khoa, et al.
Publicado: (2022)
Fine-Tuning DeepONets to Enhance Physics-informed Neural Networks for solving Partial Differential Equations
por: Wu, Sidi
Publicado: (2024)
por: Wu, Sidi
Publicado: (2024)
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
por: Ko, Seungchan, et al.
Publicado: (2024)
por: Ko, Seungchan, et al.
Publicado: (2024)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
por: Wang, Yizheng, et al.
Publicado: (2024)
por: Wang, Yizheng, et al.
Publicado: (2024)
Efficient Differentiable Approximation of Generalized Low-rank Regularization
por: Li, Naiqi, et al.
Publicado: (2025)
por: Li, Naiqi, et al.
Publicado: (2025)
Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
por: Sukumar, N., et al.
Publicado: (2024)
por: Sukumar, N., et al.
Publicado: (2024)
Physics-informed fine-tuning of foundation models for partial differential equations
por: Medvedev, Vlad, et al.
Publicado: (2026)
por: Medvedev, Vlad, et al.
Publicado: (2026)
Deep learning based numerical approximation algorithms for stochastic partial differential equations
por: Beck, Christian, et al.
Publicado: (2020)
por: Beck, Christian, et al.
Publicado: (2020)
Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
por: Su, Bowen, et al.
Publicado: (2024)
por: Su, Bowen, et al.
Publicado: (2024)
Robust identifiability for symbolic recovery of differential equations
por: Hauger, Hillary, et al.
Publicado: (2024)
por: Hauger, Hillary, et al.
Publicado: (2024)
Ejemplares similares
-
A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
por: Cardoso-Bihlo, Elsa, et al.
Publicado: (2026) -
Improving physics-informed DeepONets with hard constraints
por: Brecht, Rüdiger, et al.
Publicado: (2023) -
Physics-informed neural networks for the shallow-water equations on the sphere
por: Bihlo, Alex, et al.
Publicado: (2021) -
A shallow physics-informed neural network for solving partial differential equations on surfaces
por: Hu, Wei-Fan, et al.
Publicado: (2022) -
Diffeomorphic Neural Operator Learning
por: Taylor, Seth, et al.
Publicado: (2025)