A neural network approach for solving the Monge-Ampère equation with transport boundary condition
Fuente:
arXiv
Guardado en:
| Autores principales: | Hacking, Roel, Kusch, Lisa, Mitra, Koondanibha, Anthonissen, Martijn, IJzerman, Wilbert |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Neural-network methods for two-dimensional finite-source reflector design
por: Hacking, Roel, et al.
Publicado: (2026)
por: Hacking, Roel, et al.
Publicado: (2026)
An inverse design method for generalized zero-étendue sources and two targets
por: Braam, Pieter, et al.
Publicado: (2025)
por: Braam, Pieter, et al.
Publicado: (2025)
Design of a three-dimensional parallel-to-point imaging system based on inverse methods
por: Verma, Sanjana, et al.
Publicado: (2025)
por: Verma, Sanjana, et al.
Publicado: (2025)
Inverse freeform design of a parallel-to-two-target reflector system
por: Braam, Pieter, et al.
Publicado: (2025)
por: Braam, Pieter, et al.
Publicado: (2025)
Three-Dimensional Freeform Reflector Design with a Microfacet Surface Roughness Model
por: Kronberg, Vì, et al.
Publicado: (2024)
por: Kronberg, Vì, et al.
Publicado: (2024)
An Inverse Method for the Design of Freeform Double-Reflector Imaging Systems
por: Verma, Sanjana, et al.
Publicado: (2025)
por: Verma, Sanjana, et al.
Publicado: (2025)
Invariant deep neural networks under the finite group for solving partial differential equations
por: Zhang, Zhi-Yong, et al.
Publicado: (2024)
por: Zhang, Zhi-Yong, et al.
Publicado: (2024)
Monge-Ampère equation with Guillemin boundary condition in high dimension
por: Huang, Genggeng, et al.
Publicado: (2024)
por: Huang, Genggeng, et al.
Publicado: (2024)
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)
Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
por: Liu, Yanzhi, et al.
Publicado: (2024)
por: Liu, Yanzhi, et al.
Publicado: (2024)
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
por: Stops, Laura, et al.
Publicado: (2022)
por: Stops, Laura, et al.
Publicado: (2022)
Continuity estimates for the gradient of solutions to the Monge-Ampère equation with nature boundary conditions
por: Jian, Huaiyu, et al.
Publicado: (2024)
por: Jian, Huaiyu, 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)
Physics-informed neural networks to solve inverse problems in unbounded domains
por: Pérez-Bernal, Gregorio, et al.
Publicado: (2025)
por: Pérez-Bernal, Gregorio, et al.
Publicado: (2025)
Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks
por: Franco, Nicola Rares, et al.
Publicado: (2025)
por: Franco, Nicola Rares, et al.
Publicado: (2025)
Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics
por: Terin, Rodrigo Carmo
Publicado: (2024)
por: Terin, Rodrigo Carmo
Publicado: (2024)
The Monge-Ampère equation
por: Neilan, Michael, et al.
Publicado: (2019)
por: Neilan, Michael, et al.
Publicado: (2019)
Deep multitask neural networks for solving some stochastic optimal control problems
por: Yeo, Christian
Publicado: (2024)
por: Yeo, Christian
Publicado: (2024)
Local neural operator for solving transient partial differential equations on varied domains
por: Li, Hongyu, et al.
Publicado: (2022)
por: Li, Hongyu, et al.
Publicado: (2022)
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE
por: Deb, Nabarun, et al.
Publicado: (2025)
por: Deb, Nabarun, et al.
Publicado: (2025)
The boundary of neural network trainability is fractal
por: Sohl-Dickstein, Jascha
Publicado: (2024)
por: Sohl-Dickstein, Jascha
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)
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
por: Straub, Christopher, et al.
Publicado: (2025)
por: Straub, Christopher, et al.
Publicado: (2025)
Computing high-dimensional optimal transport by flow neural networks
por: Xu, Chen, et al.
Publicado: (2023)
por: Xu, Chen, et al.
Publicado: (2023)
On a general class of free boundary Monge-Ampère equations
por: Collins, Tristan C., et al.
Publicado: (2025)
por: Collins, Tristan C., et al.
Publicado: (2025)
Towards graph neural networks for provably solving convex optimization problems
por: Qian, Chendi, et al.
Publicado: (2025)
por: Qian, Chendi, et al.
Publicado: (2025)
Generalizing the SINDy approach with nested neural networks
por: Fiorini, Camilla, et al.
Publicado: (2024)
por: Fiorini, Camilla, et al.
Publicado: (2024)
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)
Bulk-boundary decomposition of neural networks
por: Lee, Donghee, et al.
Publicado: (2025)
por: Lee, Donghee, et al.
Publicado: (2025)
Virtual boundary integral neural network for three-dimensional exterior acoustic problems
por: Li, Jiahao, et al.
Publicado: (2026)
por: Li, Jiahao, et al.
Publicado: (2026)
BEKAN: Boundary condition-guaranteed evolutionary Kolmogorov-Arnold networks with radial basis functions for solving PDE problems
por: Kim, Bongseok, et al.
Publicado: (2025)
por: Kim, Bongseok, et al.
Publicado: (2025)
A constrained optimization approach to improve robustness of neural networks
por: Zhao, Shudian, et al.
Publicado: (2024)
por: Zhao, Shudian, et al.
Publicado: (2024)
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)
Robust and fast iterative method for the elliptic Monge-Ampère equation
por: Köhle, R. N., et al.
Publicado: (2025)
por: Köhle, R. N., et al.
Publicado: (2025)
An alternative approach to train neural networks using monotone variational inequality
por: Xu, Chen, et al.
Publicado: (2022)
por: Xu, Chen, et al.
Publicado: (2022)
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
por: Wu, Jiahao, et al.
Publicado: (2024)
por: Wu, Jiahao, et al.
Publicado: (2024)
Energy stable neural network for gradient flow equations
por: Wu, Yue, et al.
Publicado: (2023)
por: Wu, Yue, et al.
Publicado: (2023)
A novel auxiliary equation neural networks method for exactly explicit solutions of nonlinear partial differential equations
por: Yuan, Shanhao, et al.
Publicado: (2025)
por: Yuan, Shanhao, et al.
Publicado: (2025)
The second boundary value problem for a discrete Monge-Ampere equation
por: Awanou, Gerard
Publicado: (2019)
por: Awanou, Gerard
Publicado: (2019)
Complex geodesics and complex Monge--Ampère equations with boundary singularity II
por: Wang, Xieping
Publicado: (2021)
por: Wang, Xieping
Publicado: (2021)
Ejemplares similares
-
Neural-network methods for two-dimensional finite-source reflector design
por: Hacking, Roel, et al.
Publicado: (2026) -
An inverse design method for generalized zero-étendue sources and two targets
por: Braam, Pieter, et al.
Publicado: (2025) -
Design of a three-dimensional parallel-to-point imaging system based on inverse methods
por: Verma, Sanjana, et al.
Publicado: (2025) -
Inverse freeform design of a parallel-to-two-target reflector system
por: Braam, Pieter, et al.
Publicado: (2025) -
Three-Dimensional Freeform Reflector Design with a Microfacet Surface Roughness Model
por: Kronberg, Vì, et al.
Publicado: (2024)