:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Elbrächter, Dennis, Alberti, Giovanni S., Santacesaria, Matteo
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Numerical Analysis
Online Access:	https://arxiv.org/abs/2509.24710
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Localization of point scatterers via sparse optimization on measures
by: Alberti, Giovanni S., et al.
Published: (2024)

Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2025)

Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2026)

Assessing the use of Diffusion models for motion artifact correction in brain MRI
by: Angella, Paolo, et al.
Published: (2025)

The learned range test method for the inverse inclusion problem
by: Sun, Shiwei, et al.
Published: (2024)

Infinite dimensional generative sensing
by: Angella, Paolo, et al.
Published: (2026)

A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations
by: Gomez, Alvaro Almeida
Published: (2025)

Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
by: He, Ye, et al.
Published: (2026)

Solving PDEs on Unknown Manifolds with Machine Learning
by: Liang, Senwei, et al.
Published: (2021)

Manifold Learning by Mixture Models of VAEs for Inverse Problems
by: Alberti, Giovanni S., et al.
Published: (2023)

Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
by: Zhou, Hanfei, et al.
Published: (2025)

Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
by: Schonsheck, Stefan C., et al.
Published: (2022)

Foundations of Polar Linear Algebra
by: Guasti, Giovanni
Published: (2026)

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
by: Zhou, Hanfei, et al.
Published: (2025)

Manifold Filter-Combine Networks
by: Johnson, David R., et al.
Published: (2023)

A Dirac-Frenkel-Onsager principle: Instantaneous residual minimization with gauge momentum for nonlinear parametrizations of PDE solutions
by: Raviola, Matteo, et al.
Published: (2026)

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
by: Maurais, Aimee, et al.
Published: (2023)

Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
by: Salavatidezfouli, Sajad, et al.
Published: (2023)

Localized Diffusion Models
by: Gottwald, Georg A., et al.
Published: (2025)

Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
by: Zhang, Borong, et al.
Published: (2024)

Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
by: Varetti, Eugenio, et al.
Published: (2025)

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
by: Ren, Yinuo, et al.
Published: (2024)

Distillation of Discrete Diffusion through Dimensional Correlations
by: Hayakawa, Satoshi, et al.
Published: (2024)

Anderson Acceleration in Nonsmooth Problems: Local Convergence via Active Manifold Identification
by: Li, Kexin, et al.
Published: (2024)

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

Stochastic Generalized Sampling
by: Finotti, Luca, et al.
Published: (2026)

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)

Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method
by: Jacot, Arthur
Published: (2026)

Trust-Region Methods with Low-Fidelity Objective Models
by: Angino, Andrea, et al.
Published: (2025)

Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
by: Zanoni, Andrea, et al.
Published: (2024)

Function Extrapolation with Neural Networks and Its Application for Manifolds
by: Hay, Guy, et al.
Published: (2024)

Learning cardiac activation and repolarization times with operator learning
by: Centofanti, Edoardo, et al.
Published: (2025)

Energy Approach from $\varepsilon$-Graph to Continuum Diffusion Model with Connectivity Functional
by: Yang, Yahong, et al.
Published: (2025)

The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models
by: Kandasamy, Saravanan, et al.
Published: (2024)

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
by: Saporiti, Riccardo, et al.
Published: (2026)

Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
by: Alberti, Giovanni S., et al.
Published: (2022)

Manifold-based Algorithms for the Hadamard Decomposition
by: Gillis, Nicolas, et al.
Published: (2026)

A Probabilistic Framework for Solving High-Frequency Helmholtz Equations via Diffusion Models
by: Zou, Yicheng, et al.
Published: (2026)

Antithetic Noise in Diffusion Models
by: Jia, Jing, et al.
Published: (2025)