Saved in:
| Main Authors: | Xue, Na, Chen, Minghua |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.22377 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multiprecision computations with Schwarz methods
by: Outrata, Michal, et al.
Published: (2025)
by: Outrata, Michal, et al.
Published: (2025)
Spectral coefficient learning physics informed neural network for time-dependent fractional parametric differential problems
by: Sivalingam, S M, et al.
Published: (2025)
by: Sivalingam, S M, et al.
Published: (2025)
Weak and entropy physics-informed neural networks for conservation laws
by: Oubarka, Ismail, et al.
Published: (2026)
by: Oubarka, Ismail, et al.
Published: (2026)
Multilevel domain decomposition-based architectures for physics-informed neural networks
by: Dolean, Victorita, et al.
Published: (2023)
by: Dolean, Victorita, et al.
Published: (2023)
Error estimates of physics-informed neural networks for approximating Boltzmann equation
by: Abdo, Elie, et al.
Published: (2024)
by: Abdo, Elie, et al.
Published: (2024)
Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)
by: Gracyk, Andrew
Published: (2026)
Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation
by: Rahman, Mohammad Mahabubur, et al.
Published: (2025)
by: Rahman, Mohammad Mahabubur, et al.
Published: (2025)
Exact and approximate error bounds for physics-informed neural networks
by: Chantada, Augusto T., et al.
Published: (2024)
by: Chantada, Augusto T., et al.
Published: (2024)
Physics-informed neural networks for Timoshenko system with Thermoelasticity
by: Chebbi, Sabrine, et al.
Published: (2024)
by: Chebbi, Sabrine, et al.
Published: (2024)
IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
by: Zheng, Jiachun, et al.
Published: (2025)
by: Zheng, Jiachun, et al.
Published: (2025)
Optimal time sampling in physics-informed neural networks
by: Turinici, Gabriel
Published: (2024)
by: Turinici, Gabriel
Published: (2024)
Sensitivity analysis using Physics-informed neural networks
by: Hanna, John M., et al.
Published: (2023)
by: Hanna, John M., et al.
Published: (2023)
Causality-guided adaptive sampling method for physics-informed neural networks
by: Lin, Shuning, et al.
Published: (2024)
by: Lin, Shuning, et al.
Published: (2024)
A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations
by: Murari, K., et al.
Published: (2025)
by: Murari, K., et al.
Published: (2025)
Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs
by: Zhang, Hao, et al.
Published: (2024)
by: Zhang, Hao, et al.
Published: (2024)
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
by: Heinlein, Alexander, et al.
Published: (2025)
by: Heinlein, Alexander, et al.
Published: (2025)
Physics-informed neural networks for solving two-phase flow problems with moving interfaces
by: Zhai, Qijia, et al.
Published: (2026)
by: Zhai, Qijia, et al.
Published: (2026)
A physics-informed neural network framework for modeling obstacle-related equations
by: Bahja, Hamid El, et al.
Published: (2023)
by: Bahja, Hamid El, et al.
Published: (2023)
A matrix preconditioning framework for physics-informed neural networks based on adjoint method
by: Song, Jiahao, et al.
Published: (2025)
by: Song, Jiahao, et al.
Published: (2025)
Certified machine learning: A posteriori error estimation for physics-informed neural networks
by: Hillebrecht, Birgit, et al.
Published: (2022)
by: Hillebrecht, Birgit, et al.
Published: (2022)
A shallow physics-informed neural network for solving partial differential equations on surfaces
by: Hu, Wei-Fan, et al.
Published: (2022)
by: Hu, Wei-Fan, et al.
Published: (2022)
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
by: Demo, Nicola, et al.
Published: (2021)
by: Demo, Nicola, et al.
Published: (2021)
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
by: De Ryck, Tim, et al.
Published: (2024)
by: De Ryck, Tim, et al.
Published: (2024)
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)
by: Shekarpaz, Simin, et al.
Published: (2023)
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
by: Guo, Yuan, et al.
Published: (2025)
by: Guo, Yuan, et al.
Published: (2025)
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)
by: Fanaskov, Vladimir, et al.
Published: (2024)
High-order BDF convolution quadrature for stochastic fractional evolution equations driven by integrated additive noise
by: Chen, Minghua, et al.
Published: (2024)
by: Chen, Minghua, et al.
Published: (2024)
A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
by: Dick, Josef, et al.
Published: (2025)
by: Dick, Josef, et al.
Published: (2025)
Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
by: Marcondes, Diego
Published: (2026)
by: Marcondes, Diego
Published: (2026)
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
by: Lin, Anci, et al.
Published: (2026)
by: Lin, Anci, et al.
Published: (2026)
Physics-informed neural networks for operator equations with stochastic data
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
Physics informed neural network for forward and inverse radiation heat transfer in graded-index medium
by: Murari, K., et al.
Published: (2024)
by: Murari, K., et al.
Published: (2024)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
by: Liu, Ye, et al.
Published: (2024)
by: Liu, Ye, et al.
Published: (2024)
Improving the accuracy of physics-informed neural networks via last-layer retraining
by: Qadeer, Saad, et al.
Published: (2026)
by: Qadeer, Saad, et al.
Published: (2026)
An extrapolation-driven network architecture for physics-informed deep learning
by: Wang, Yong, et al.
Published: (2024)
by: Wang, Yong, et al.
Published: (2024)
Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks
by: Yu, Zan, et al.
Published: (2024)
by: Yu, Zan, et al.
Published: (2024)
Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators
by: Jiao, Anran, et al.
Published: (2024)
by: Jiao, Anran, et al.
Published: (2024)
Convergence of physics-informed neural networks modeling time-harmonic wave fields
by: Schoder, Stefan, et al.
Published: (2025)
by: Schoder, Stefan, et al.
Published: (2025)
Fast training of accurate physics-informed neural networks without gradient descent
by: Datar, Chinmay, et al.
Published: (2024)
by: Datar, Chinmay, et al.
Published: (2024)
Similar Items
-
Multiprecision computations with Schwarz methods
by: Outrata, Michal, et al.
Published: (2025) -
Spectral coefficient learning physics informed neural network for time-dependent fractional parametric differential problems
by: Sivalingam, S M, et al.
Published: (2025) -
Weak and entropy physics-informed neural networks for conservation laws
by: Oubarka, Ismail, et al.
Published: (2026) -
Multilevel domain decomposition-based architectures for physics-informed neural networks
by: Dolean, Victorita, et al.
Published: (2023) -
Error estimates of physics-informed neural networks for approximating Boltzmann equation
by: Abdo, Elie, et al.
Published: (2024)