Cover Image

Adapting Noise to Data: Generative Flows from 1D Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chemseddine, Jannis, Kornhardt, Gregor, Duong, Richard, Steidl, Gabriele
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Analysis of PDEs
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
by: Chemseddine, Jannis, et al.
Published: (2024)

Spherical Flows for Sampling Categorical Data
by: Chemseddine, Jannis, et al.
Published: (2026)

Self-Aware Markov Models for Discrete Reasoning
by: Kornhardt, Gregor, et al.
Published: (2026)

Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functions
by: Duong, Richard, et al.
Published: (2024)

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
by: Chemseddine, Jannis, et al.
Published: (2024)

Trajectory Generator Matching for Time Series
by: Jahn, T., et al.
Published: (2025)

Wasserstein Gradient Flows of MMD Functionals with Distance Kernels under Sobolev Regularization
by: Duong, Richard, et al.
Published: (2024)

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
by: Hagemann, Paul, et al.
Published: (2023)

Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
by: Piening, Moritz, et al.
Published: (2026)

Interaction-Force Transport Gradient Flows
by: Gladin, Egor, et al.
Published: (2024)

Kernel Approximation of Fisher-Rao Gradient Flows
by: Zhu, Jia-Jie, et al.
Published: (2024)

Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
by: Rux, Nicolaj, et al.
Published: (2025)

Trace Regularity PINNs: Enforcing $\mathrm{H}^{\frac{1}{2}}(\partial Ω)$ for Boundary Data
by: Kim, Doyoon, et al.
Published: (2025)

Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
by: Kaiser, Luis, et al.
Published: (2024)

Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
by: Dai, Yilong, et al.
Published: (2026)

Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
by: Taniguchi, Koichi, et al.
Published: (2026)

Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates
by: Mooney, Connor, et al.
Published: (2024)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)

Gradient Flow Sampler-based Distributionally Robust Optimization
by: Xu, Zusen, et al.
Published: (2025)

Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)

Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
by: Carrillo, José A., et al.
Published: (2024)

Mixed Noise and Posterior Estimation with Conditional DeepGEM
by: Hagemann, Paul, et al.
Published: (2024)

Learning functional components of PDEs from data using neural networks
by: Loman, Torkel E., et al.
Published: (2026)

Regularized Stein Variational Gradient Flow
by: He, Ye, et al.
Published: (2022)

Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
by: Wald, Christian, et al.
Published: (2025)

Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds
by: Trillos, Nicolás García, et al.
Published: (2025)

Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
by: Zadgar, Arman, et al.
Published: (2025)

Stiff Transfer Learning for Physics-Informed Neural Networks
by: Seiler, Emilien, et al.
Published: (2025)

Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
by: Nooraiepour, Mohammad, et al.
Published: (2026)

Is Zero-Shot Super-Resolution Possible in Operator Learning?
by: Subedi, Unique, et al.
Published: (2026)

The emergence of clusters in self-attention dynamics
by: Geshkovski, Borjan, et al.
Published: (2023)

Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery
by: Yu, Yue, et al.
Published: (2024)

On Probabilistic Embeddings in Optimal Dimension Reduction
by: Murray, Ryan, et al.
Published: (2024)

Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
by: Chen, Ziang, et al.
Published: (2024)

Improved Graph-based semi-supervised learning Schemes
by: Bozorgnia, Farid
Published: (2024)

Solving the Poisson Equation with Dirichlet data by shallow ReLU$^α$-networks: A regularity and approximation perspective
by: Vaishampayan, Malhar, et al.
Published: (2024)

Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
by: Yang, Wenqiang, et al.
Published: (2026)

A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
by: Kathane, Shivprasad, et al.
Published: (2024)

Regularity of Solutions to Beckmann's Parametric Optimal Transport
by: Gottschalk, Hanno, et al.
Published: (2026)

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