Buchumschlag

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Zeudong, Etienne, Cardoso-Bihlo, Elsa, Bihlo, Alex
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning Numerical Analysis
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
von: Cardoso-Bihlo, Elsa, et al.
Veröffentlicht: (2026)

Improving physics-informed DeepONets with hard constraints
von: Brecht, Rüdiger, et al.
Veröffentlicht: (2023)

Physics-informed neural networks for the shallow-water equations on the sphere
von: Bihlo, Alex, et al.
Veröffentlicht: (2021)

A shallow physics-informed neural network for solving partial differential equations on surfaces
von: Hu, Wei-Fan, et al.
Veröffentlicht: (2022)

Diffeomorphic Neural Operator Learning
von: Taylor, Seth, et al.
Veröffentlicht: (2025)

Learning vertical coordinates via automatic differentiation of a dynamical core
von: Whittaker, Tim, et al.
Veröffentlicht: (2025)

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
von: Williams, Emily, et al.
Veröffentlicht: (2024)

Learning by solving differential equations
von: Dherin, Benoit, et al.
Veröffentlicht: (2025)

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
von: Liu, Ye, et al.
Veröffentlicht: (2024)

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios
von: Yang, Bo, et al.
Veröffentlicht: (2025)

Deep Neural networks for solving high-dimensional parabolic partial differential equations
von: Zhang, Wenzhong, et al.
Veröffentlicht: (2026)

Physics-informed neural networks for tsunami inundation modeling
von: Brecht, Rüdiger, et al.
Veröffentlicht: (2024)

CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
von: Berman, Jules, et al.
Veröffentlicht: (2024)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
von: Zheng, Haoyang, et al.
Veröffentlicht: (2023)

DeepONet-accelerated Bayesian inversion for moving boundary problems
von: Iglesias, Marco A., et al.
Veröffentlicht: (2025)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
von: Moya, Christian, et al.
Veröffentlicht: (2024)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
von: Yang, Yahong
Veröffentlicht: (2024)

NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
von: Levrero-Florencio, Francesc, et al.
Veröffentlicht: (2026)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
von: Zhang, Huan, et al.
Veröffentlicht: (2024)

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
von: Blechschmidt, Jan, et al.
Veröffentlicht: (2025)

Pseudo-differential-enhanced physics-informed neural networks
von: Gracyk, Andrew
Veröffentlicht: (2026)

A randomized algorithm to solve reduced rank operator regression
von: Turri, Giacomo, et al.
Veröffentlicht: (2023)

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
von: Xu, Tengfei, et al.
Veröffentlicht: (2023)

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
von: Matthews, Jason, et al.
Veröffentlicht: (2024)

Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
von: Thanasutives, Pongpisit, et al.
Veröffentlicht: (2024)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
von: Jiang, Zhaoxi, et al.
Veröffentlicht: (2025)

A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
von: Han, Jiequn, et al.
Veröffentlicht: (2025)

ForeCite: Adapting Pre-Trained Language Models to Predict Future Citation Rates of Academic Papers
von: Hull, Gavin, et al.
Veröffentlicht: (2025)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
von: Son, Hwijae
Veröffentlicht: (2025)

fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
von: Lu, Binghang, et al.
Veröffentlicht: (2026)

Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
von: Nguyen, Thi Nguyen Khoa, et al.
Veröffentlicht: (2022)

Fine-Tuning DeepONets to Enhance Physics-informed Neural Networks for solving Partial Differential Equations
von: Wu, Sidi
Veröffentlicht: (2024)

VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
von: Ko, Seungchan, et al.
Veröffentlicht: (2024)

Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
von: Wang, Yizheng, et al.
Veröffentlicht: (2024)

Efficient Differentiable Approximation of Generalized Low-rank Regularization
von: Li, Naiqi, et al.
Veröffentlicht: (2025)

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
von: Sukumar, N., et al.
Veröffentlicht: (2024)

Physics-informed fine-tuning of foundation models for partial differential equations
von: Medvedev, Vlad, et al.
Veröffentlicht: (2026)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
von: Beck, Christian, et al.
Veröffentlicht: (2020)

Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
von: Su, Bowen, et al.
Veröffentlicht: (2024)

Robust identifiability for symbolic recovery of differential equations
von: Hauger, Hillary, et al.
Veröffentlicht: (2024)