Cover Image

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yao, Jiachen, Mammadov, Abbas, Berner, Julius, Kerrigan, Gavin, Ye, Jong Chul, Azizzadenesheli, Kamyar, Anandkumar, Anima
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
by: Berner, Julius, et al.
Published: (2025)

Decoupled Diffusion Sampling for Inverse Problems on Function Spaces
by: Lin, Thomas Y. L., et al.
Published: (2026)

Neural Operators with Localized Integral and Differential Kernels
by: Liu-Schiaffini, Miguel, et al.
Published: (2024)

Solving Poisson Equations using Neural Walk-on-Spheres
by: Nam, Hong Chul, et al.
Published: (2024)

Neural Operator: Learning Maps Between Function Spaces
by: Kovachki, Nikola, et al.
Published: (2021)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
by: Tu, Renbo, et al.
Published: (2023)

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)

Equivariant Graph Neural Operator for Modeling 3D Dynamics
by: Xu, Minkai, et al.
Published: (2024)

High precision PINNs in unbounded domains: application to singularity formulation in PDEs
by: Wang, Yixuan, et al.
Published: (2025)

DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
by: Hao, Zhongkai, et al.
Published: (2024)

Dynamical Measure Transport and Neural PDE Solvers for Sampling
by: Sun, Jingtong, et al.
Published: (2024)

EquiReg: Equivariance Regularized Diffusion for Inverse Problems
by: Tolooshams, Bahareh, et al.
Published: (2025)

Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
by: Ma, Ziqi, et al.
Published: (2024)

Robust Physics-Guided Diffusion for Full-Waveform Inversion
by: Peng, Jishen, et al.
Published: (2026)

ODE-DPS: ODE-based Diffusion Posterior Sampling for Inverse Problems in Partial Differential Equation
by: Jiang, Enze, et al.
Published: (2024)

RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
by: Bassi, Hardeep, et al.
Published: (2025)

Amortized Posterior Sampling with Diffusion Prior Distillation
by: Mammadov, Abbas, et al.
Published: (2024)

Bayesian Interpolating Neural Network (B-INN): a scalable and reliable Bayesian model for large-scale physical systems
by: Park, Chanwook, et al.
Published: (2026)

Autoregression-Free Neural Operators for Time-Dependent PDEs
by: Zhang, Jiaquan, et al.
Published: (2026)

On the Well-Posedness of Green's Function Reconstruction via the Kirchhoff-Helmholtz Equation for One-Speed Neutron Diffusion
by: Ponciroli, Roberto
Published: (2025)

Applications of a space-time FOSLS formulation for parabolic PDEs
by: Gantner, Gregor, et al.
Published: (2022)

Sequential Controlled Langevin Diffusions
by: Chen, Junhua, et al.
Published: (2024)

Functional tensor train neural network for solving high-dimensional PDEs
by: Feng, Yani, et al.
Published: (2025)

Solving PDEs With Deep Neural Nets under General Boundary Conditions
by: Zhang, Chenggong
Published: (2025)

Predictive Moving Sample Method for Physics-Informed Neural Solvers of Time-Dependent PDEs
by: Xu, Beining, et al.
Published: (2026)

Neural Evolutionary Kernel Method: A Knowledge-Guided Framework for Solving Evolutionary PDEs
by: Ling, Shuo, et al.
Published: (2026)

An Adaptive CUR Algorithm and its Application to Reduced-Order Modeling of Random PDEs
by: Palkar, Grishma, et al.
Published: (2025)

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs
by: De Ryck, T., et al.
Published: (2025)

Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions
by: Chen, Ziang, et al.
Published: (2025)

Regularity of Second-Order Elliptic PDEs in Spectral Barron Spaces
by: Chen, Ziang, et al.
Published: (2026)

Smoothed Circulant Embedding with Applications to Multilevel Monte Carlo Methods for PDEs with Random Coefficients
by: Istratuca, Anastasia, et al.
Published: (2023)

Stochastic Finite Volume Approximation with Clustering in the Parameter Space for Forward Uncertainty Quantification of PDEs with Random Parameters
by: Zhang, Zhao, et al.
Published: (2025)

Stable Mesh-Free Variational Radial Basis Function Approximation for Elliptic PDEs and Obstacle Problems
by: Le, Tan Phuong Dong, et al.
Published: (2026)

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
by: Tang, Kejun, et al.
Published: (2023)

Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
by: Zhang, Bingliang, et al.
Published: (2024)

IMEX-RK finite volume methods for nonlinear 1d parabolic PDEs. Application to option pricing
by: López-Salas, J. G., et al.
Published: (2024)

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
by: Shao, Zihan, et al.
Published: (2025)

Score-based Diffusion Models in Function Space
by: Lim, Jae Hyun, et al.
Published: (2023)

Antisymmetry, pseudospectral methods, and conservative PDEs
by: McLachlan, Robert, et al.
Published: (1999)