Imagen de Portada

Improving physics-informed DeepONets with hard constraints

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Brecht, Rüdiger, Popovych, Dmytro R., Bihlo, Alex, Popovych, Roman O.
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Numerical Analysis Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
por: Zeudong, Etienne, et al.
Publicado: (2025)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
por: Jiang, Zhaoxi, 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)

Generalized symmetries of Burgers equation
por: Popovych, Dmytro R., et al.
Publicado: (2024)

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

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

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

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

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

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

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

The Calderón's problem via DeepONets
por: Castro, Javier, et al.
Publicado: (2022)

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

Scaling physics-informed hard constraints with mixture-of-experts
por: Chalapathi, Nithin, et al.
Publicado: (2024)

Algebraic method of group classification for semi-normalized classes of differential equations
por: Kurujyibwami, Celestin, et al.
Publicado: (2024)

Optimal time sampling in physics-informed neural networks
por: Turinici, Gabriel
Publicado: (2024)

XI-DeepONet: An operator learning method for elliptic interface problems
por: Bi, Ran, et al.
Publicado: (2024)

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

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

Hybrid Least Squares/Gradient Descent Methods for DeepONets
por: Choi, Jun, et al.
Publicado: (2025)

BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet
por: Li, Lingfeng, et al.
Publicado: (2024)

Physically consistent predictive reduced-order modeling by enhancing Operator Inference with state constraints
por: Kim, Hyeonghun, et al.
Publicado: (2025)

Symmetry analysis and exact solutions of multi-layer quasi-geostrophic problem
por: Koval, Serhii D., et al.
Publicado: (2026)

Astral: training physics-informed neural networks with error majorants
por: Fanaskov, Vladimir, et al.
Publicado: (2024)

Parameterized Physics-informed Neural Networks for Parameterized PDEs
por: Cho, Woojin, et al.
Publicado: (2024)

Topological DeepONets and a generalization of the Chen-Chen operator approximation theorem
por: Ismailov, Vugar
Publicado: (2026)

High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
por: Yuan, Biao, et al.
Publicado: (2025)

A POD-DeepONet Framework for Forward and Inverse Design of 2D Photonic Crystals
por: Wang, Yueqi, et al.
Publicado: (2026)

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration
por: Lee, Jonathan E., et al.
Publicado: (2024)

Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
por: Shekarpaz, Simin, et al.
Publicado: (2023)

Deep Learning Method for Computing Committor Functions with Adaptive Sampling
por: Lin, Bo, et al.
Publicado: (2024)

Deciphering and integrating invariants for neural operator learning with various physical mechanisms
por: Zhang, Rui, et al.
Publicado: (2023)

Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
por: Lin, Bo, et al.
Publicado: (2024)

R-adaptive DeepONet: Learning Solution Operators for PDEs with Discontinuous Solutions Using an R-adaptive Strategy
por: Zhu, Yameng, et al.
Publicado: (2024)

Generalized symmetries of remarkable (1+2)-dimensional Fokker-Planck equation
por: Popovych, Dmytro R., et al.
Publicado: (2024)

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
por: Chen, Zhuo, et al.
Publicado: (2024)

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
por: Cao, Lianghao, et al.
Publicado: (2024)

Extended symmetry analysis of remarkable (1+2)-dimensional Fokker-Planck equation
por: Koval, Serhii D., et al.
Publicado: (2022)