Generative diffusion learning for parametric partial differential equations
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Ting, Plechac, Petr, Knap, Jaroslaw |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the optimality of dimension truncation error rates for a class of parametric partial differential equations
by: Guth, Philipp A., et al.
Published: (2025)
by: Guth, Philipp A., et al.
Published: (2025)
Single-shot prediction of parametric partial differential equations
by: Rafiq, Khalid, et al.
Published: (2025)
by: Rafiq, Khalid, et al.
Published: (2025)
Nemytskii neural operator: a nonlinear model reduction method for parametrized partial differential equations
by: Li, Jingye, et al.
Published: (2025)
by: Li, Jingye, et al.
Published: (2025)
Physics informed learning of orthogonal features with applications in solving partial differential equations
by: Jia, Qianxing, et al.
Published: (2026)
by: Jia, Qianxing, et al.
Published: (2026)
Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools
by: Karnakov, Petr, et al.
Published: (2022)
by: Karnakov, Petr, et al.
Published: (2022)
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
by: Jentzen, Arnulf, et al.
Published: (2023)
by: Jentzen, Arnulf, et al.
Published: (2023)
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
by: Zhang, Huan, et al.
Published: (2024)
by: Zhang, Huan, et al.
Published: (2024)
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
by: Thanasutives, Pongpisit, et al.
Published: (2024)
by: Thanasutives, Pongpisit, et al.
Published: (2024)
POD-based reduced order methods for optimal control problems governed by parametric partial differential equation with varying boundary control
by: Strazzullo, Maria, et al.
Published: (2022)
by: Strazzullo, Maria, et al.
Published: (2022)
Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)
by: Eidnes, Sølve, et al.
Published: (2023)
Noise-robust multi-fidelity surrogate modelling for parametric partial differential equations
by: Kent, Benjamin M., et al.
Published: (2025)
by: Kent, Benjamin M., et al.
Published: (2025)
Optimal Prediction for Hamiltonian partial differential equations
by: Chorin, A. J., et al.
Published: (1999)
by: Chorin, A. J., et al.
Published: (1999)
Approximating partial differential equations without boundary conditions
by: Bonito, Andrea, et al.
Published: (2024)
by: Bonito, Andrea, et al.
Published: (2024)
Boundary neuron method for solving partial differential equations
by: Lin, Ye, et al.
Published: (2026)
by: Lin, Ye, et al.
Published: (2026)
An alternating low-rank projection approach for partial differential equations with random inputs
by: Wang, Guanjie, et al.
Published: (2024)
by: Wang, Guanjie, et al.
Published: (2024)
Optimally accurate operators for partial differential equations
by: Fuji, Nobuaki, et al.
Published: (2025)
by: Fuji, Nobuaki, et al.
Published: (2025)
Solving partial differential equations in participating media
by: Miller, Bailey, et al.
Published: (2025)
by: Miller, Bailey, et al.
Published: (2025)
Data-integrated neural networks for solving partial differential equations
by: Zheng, Jiachun, et al.
Published: (2025)
by: Zheng, Jiachun, et al.
Published: (2025)
An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients
by: Zhu, Yujun, et al.
Published: (2026)
by: Zhu, Yujun, et al.
Published: (2026)
Adaptive feature capture method for solving partial differential equations with near singular solutions
by: Deng, Yangtao, et al.
Published: (2025)
by: Deng, Yangtao, et al.
Published: (2025)
An introduction to the a posteriori error analysis of parabolic partial differential equations
by: Smears, Iain
Published: (2025)
by: Smears, Iain
Published: (2025)
Rank Inspired Neural Network for solving linear partial differential equations
by: Peng, Wentao, et al.
Published: (2025)
by: Peng, Wentao, et al.
Published: (2025)
Subspace method based on neural networks for solving the partial differential equation
by: Xu, Zhaodong, et al.
Published: (2024)
by: Xu, Zhaodong, et al.
Published: (2024)
Moving sample method for solving time-dependent partial differential equations
by: Xu, Beining, et al.
Published: (2026)
by: Xu, Beining, et al.
Published: (2026)
Spatial discretization of partial differential equations with integrals
by: McLachlan, Robert I
Published: (1998)
by: McLachlan, Robert I
Published: (1998)
Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)
by: Beck, Christian, et al.
Published: (2020)
An adaptive ANOVA stochastic Galerkin method for partial differential equations with high-dimensional random inputs
by: Wang, Guanjie, et al.
Published: (2023)
by: Wang, Guanjie, et al.
Published: (2023)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)
by: Xu, Tengfei, et al.
Published: (2023)
Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
by: Sharma, Harsh, et al.
Published: (2025)
by: Sharma, Harsh, et al.
Published: (2025)
A backward Monte-Carlo method for solving parabolic partial differential equations
by: Carlsson, Johan
Published: (2000)
by: Carlsson, Johan
Published: (2000)
A posteriori error estimates for parabolic partial differential equations on stationary surfaces
by: Kovács, Balázs, et al.
Published: (2024)
by: Kovács, Balázs, et al.
Published: (2024)
Well-balanced convex limiting for finite element discretizations of steady convection-diffusion-reaction equations
by: Knobloch, Petr, et al.
Published: (2024)
by: Knobloch, Petr, et al.
Published: (2024)
Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis
by: Ballini, Enrico, et al.
Published: (2025)
by: Ballini, Enrico, et al.
Published: (2025)
Quantum Circuits for partial differential equations via Schrödingerisation
by: Hu, Junpeng, et al.
Published: (2024)
by: Hu, Junpeng, et al.
Published: (2024)
On Lattice Boltzmann Methods based on vector-kinetic models for hyperbolic partial differential equations
by: Anandan, Megala, et al.
Published: (2024)
by: Anandan, Megala, et al.
Published: (2024)
Point collocation with mollified piecewise polynomial approximants for high-order partial differential equations
by: Alfarisy, Dewangga, et al.
Published: (2024)
by: Alfarisy, Dewangga, et al.
Published: (2024)
Deep neural network approximation for high-dimensional parabolic partial integro-differential equations
by: Baranek, Marcin
Published: (2025)
by: Baranek, Marcin
Published: (2025)
High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations
by: Beneventano, Pierfrancesco, et al.
Published: (2020)
by: Beneventano, Pierfrancesco, et al.
Published: (2020)
Subspace method based on neural networks for solving the partial differential equation in weak form
by: Liu, Pengyuan, et al.
Published: (2024)
by: Liu, Pengyuan, et al.
Published: (2024)
On the efficiency of a posteriori error estimators for parabolic partial differential equations in the energy norm
by: Smears, Iain
Published: (2025)
by: Smears, Iain
Published: (2025)
Similar Items
-
On the optimality of dimension truncation error rates for a class of parametric partial differential equations
by: Guth, Philipp A., et al.
Published: (2025) -
Single-shot prediction of parametric partial differential equations
by: Rafiq, Khalid, et al.
Published: (2025) -
Nemytskii neural operator: a nonlinear model reduction method for parametrized partial differential equations
by: Li, Jingye, et al.
Published: (2025) -
Physics informed learning of orthogonal features with applications in solving partial differential equations
by: Jia, Qianxing, et al.
Published: (2026) -
Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools
by: Karnakov, Petr, et al.
Published: (2022)