HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
Fuente:
arXiv
Guardado en:
| Autores principales: | Zheng, Haoyang, Huang, Yao, Huang, Ziyang, Hao, Wenrui, Lin, Guang |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
por: Zheng, Jiachun, et al.
Publicado: (2025)
por: Zheng, Jiachun, et al.
Publicado: (2025)
Data-integrated neural networks for solving partial differential equations
por: Zheng, Jiachun, et al.
Publicado: (2025)
por: Zheng, Jiachun, et al.
Publicado: (2025)
Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons
por: Hao, Wenrui, et al.
Publicado: (2023)
por: Hao, Wenrui, et al.
Publicado: (2023)
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)
Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs
por: Zhang, Hao, et al.
Publicado: (2024)
por: Zhang, Hao, et al.
Publicado: (2024)
SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
por: Linghu, Jiale, et al.
Publicado: (2025)
por: Linghu, Jiale, et al.
Publicado: (2025)
A piecewise neural network method for solving large interval solution to initial value problem of ordinary differential equations
por: Han, Dongpeng, et al.
Publicado: (2024)
por: Han, Dongpeng, et al.
Publicado: (2024)
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)
A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations
por: Murari, K., et al.
Publicado: (2025)
por: Murari, K., et al.
Publicado: (2025)
Adaptive neural network basis methods for partial differential equations with low-regular solutions
por: Huang, Jianguo, et al.
Publicado: (2024)
por: Huang, Jianguo, et al.
Publicado: (2024)
Subspace method based on neural networks for solving the partial differential equation
por: Xu, Zhaodong, et al.
Publicado: (2024)
por: Xu, Zhaodong, et al.
Publicado: (2024)
HANN: Homotopy auxiliary neural network for solving nonlinear algebraic equations
por: Zai, Ling-Zhe, et al.
Publicado: (2025)
por: Zai, Ling-Zhe, et al.
Publicado: (2025)
The discrete inverse conductivity problem solved by the weights of an interpretable neural network
por: Beretta, Elena, et al.
Publicado: (2024)
por: Beretta, Elena, et al.
Publicado: (2024)
Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations
por: Zhang, Zhengqi, et al.
Publicado: (2024)
por: Zhang, Zhengqi, et al.
Publicado: (2024)
Fourier heuristic PINNs to solve the biharmonic equations based on its coupled scheme
por: Huang, Yujia, et al.
Publicado: (2025)
por: Huang, Yujia, et al.
Publicado: (2025)
Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems
por: Qiu, Lin, et al.
Publicado: (2024)
por: Qiu, Lin, et al.
Publicado: (2024)
Subspace method based on neural networks for solving the partial differential equation in weak form
por: Liu, Pengyuan, et al.
Publicado: (2024)
por: Liu, Pengyuan, et al.
Publicado: (2024)
Neural operators for solving nonlinear inverse problems
por: Scherzer, Otmar, et al.
Publicado: (2025)
por: Scherzer, Otmar, et al.
Publicado: (2025)
Quadratic neural networks for solving inverse problems
por: Frischauf, Leon, et al.
Publicado: (2023)
por: Frischauf, Leon, et al.
Publicado: (2023)
Physics-informed neural networks for solving two-phase flow problems with moving interfaces
por: Zhai, Qijia, et al.
Publicado: (2026)
por: Zhai, Qijia, et al.
Publicado: (2026)
Partial‐differential‐algebraic equations of nonlinear dynamics by physics‐informed neural‐network: (I) Operator splitting and framework assessment
por: Loc Vu‐Quoc, et al.
Publicado: (2024)
por: Loc Vu‐Quoc, et al.
Publicado: (2024)
A neural operator framework for solving inverse scattering problems
por: Chenu, Victor, et al.
Publicado: (2026)
por: Chenu, Victor, et al.
Publicado: (2026)
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)
Pseudo-differential-enhanced physics-informed neural networks
por: Gracyk, Andrew
Publicado: (2026)
por: Gracyk, Andrew
Publicado: (2026)
Spectral coefficient learning physics informed neural network for time-dependent fractional parametric differential problems
por: Sivalingam, S M, et al.
Publicado: (2025)
por: Sivalingam, S M, et al.
Publicado: (2025)
Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
por: Shahab, Muhammad Luthfi, et al.
Publicado: (2026)
por: Shahab, Muhammad Luthfi, et al.
Publicado: (2026)
Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
por: Zhuang, Bozhou, et al.
Publicado: (2024)
por: Zhuang, Bozhou, et al.
Publicado: (2024)
Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
por: Zeudong, Etienne, et al.
Publicado: (2025)
por: Zeudong, Etienne, et al.
Publicado: (2025)
Rank Inspired Neural Network for solving linear partial differential equations
por: Peng, Wentao, et al.
Publicado: (2025)
por: Peng, Wentao, et al.
Publicado: (2025)
Error estimates of physics-informed neural networks for approximating Boltzmann equation
por: Abdo, Elie, et al.
Publicado: (2024)
por: Abdo, Elie, et al.
Publicado: (2024)
A novel number-theoretic sampling method for neural network solutions of partial differential equations
por: Yang, Yu, et al.
Publicado: (2024)
por: Yang, Yu, et al.
Publicado: (2024)
Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators
por: Jiao, Anran, et al.
Publicado: (2024)
por: Jiao, Anran, et al.
Publicado: (2024)
Physics informed learning of orthogonal features with applications in solving partial differential equations
por: Jia, Qianxing, et al.
Publicado: (2026)
por: Jia, Qianxing, et al.
Publicado: (2026)
Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation
por: Rahman, Mohammad Mahabubur, et al.
Publicado: (2025)
por: Rahman, Mohammad Mahabubur, et al.
Publicado: (2025)
StPINNs - Deep learning framework for approximation of stochastic differential equations
por: Baranek, Marcin, et al.
Publicado: (2025)
por: Baranek, Marcin, et al.
Publicado: (2025)
Double-activation neural network for solving parabolic equations with time delay
por: Huang, Qiumei, et al.
Publicado: (2024)
por: Huang, Qiumei, et al.
Publicado: (2024)
Improved randomized neural network methods with boundary processing for solving elliptic equations
por: Zhou, Huifang, et al.
Publicado: (2024)
por: Zhou, Huifang, et al.
Publicado: (2024)
On the recovery of two function-valued coefficients in the Helmholtz equation for inverse scattering problems via neural networks
por: Zhou, Zehui
Publicado: (2024)
por: Zhou, Zehui
Publicado: (2024)
Adaptive feature capture method for solving partial differential equations with near singular solutions
por: Deng, Yangtao, et al.
Publicado: (2025)
por: Deng, Yangtao, et al.
Publicado: (2025)
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)
Ejemplares similares
-
IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
por: Zheng, Jiachun, et al.
Publicado: (2025) -
Data-integrated neural networks for solving partial differential equations
por: Zheng, Jiachun, et al.
Publicado: (2025) -
Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons
por: Hao, Wenrui, et al.
Publicado: (2023) -
A shallow physics-informed neural network for solving partial differential equations on surfaces
por: Hu, Wei-Fan, et al.
Publicado: (2022) -
Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs
por: Zhang, Hao, et al.
Publicado: (2024)