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We study a class of semilinear diffusion equations on infinite, connected, weighted graphs, focusing on two types of nonlinearities: monotone decreasing and Lipschitz continuous. Under minimal structural assumptions on the graph, we…

Analysis of PDEs · Mathematics 2026-05-15 Elvise Berchio , Davide Bianchi , Alberto G. Setti , Maria Vallarino

Local diffusion coefficients in disordered systems such as spin glass systems and living cells are highly heterogeneous and may change over time. Such a time-dependent and spatially heterogeneous environment results in irreproducibility of…

Statistical Mechanics · Physics 2016-12-21 Takuma Akimoto , Eiji Yamamoto

We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introduce new trainable parameters, leading to deployment…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Tripti Shukla , Srikrishna Karanam , Balaji Vasan Srinivasan

The diffusion bridge, which is a diffusion process conditioned on hitting a specific state within a finite period, has found broad applications in various scientific and engineering fields. However, simulating diffusion bridges for modeling…

Machine Learning · Computer Science 2025-05-02 Gefan Yang , Elizabeth Louise Baker , Michael L. Severinsen , Christy Anna Hipsley , Stefan Sommer

For diffusive systems that can be described by fluctuating hydrodynamics and by the Macroscopic Fluctuation Theory of Bertini et al., the total current fluctuations display universal features when the system is closed and in equilibrium.…

Statistical Mechanics · Physics 2011-11-29 Vivien Lecomte , Alberto Imparato , Frédéric Van Wijland

We investigate a class of systems of partial differential equations with nonlinear cross-diffusion and nonlocal interactions, which are of interest in several contexts in social sciences, finance, biology, and real world applications.…

Analysis of PDEs · Mathematics 2017-10-05 M. Di Francesco , A. Esposito , S. Fagioli

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied…

Machine Learning · Computer Science 2025-03-14 Jan-Hendrik Bastek , WaiChing Sun , Dennis M. Kochmann

We introduce a universal diffusion-based downscaling framework that lifts deterministic low-resolution weather forecasts into probabilistic high-resolution predictions without any model-specific fine-tuning. A single conditional diffusion…

Machine Learning · Computer Science 2026-04-21 Roberto Molinaro , Niall Siegenheim , Henry Martin , Mark Frey , Niels Poulsen , Philipp Seitz , Marvin Vincent Gabler

For a uniform process $\{ X_t: t\in E\}$ (by which $X_t $ is uniformly distributed on $(0,1)$ for $t\in E$) and a function $w(x)>0$ on $(0,1)$, we give a sufficient condition for the weak convergence of the empirical process based on $\{…

Probability · Mathematics 2014-12-30 Yuping Yang

We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one. Such constraints arise naturally in safety-critical applications and in rare-event…

Artificial Intelligence · Computer Science 2026-03-10 Zhengyi Guo , Wenpin Tang , Renyuan Xu

We study the distribution of the unobserved states of two measure-valued diffusions of Fleming-Viot and Dawson-Watanabe type, conditional on observations from the underlying populations collected at past, present and future times. If seen…

Statistics Theory · Mathematics 2026-01-07 Filippo Ascolani , Antonio Lijoi , Matteo Ruggiero

We consider the task of generating discrete-time realisations of a nonlinear multivariate diffusion process satisfying an It\^o stochastic differential equation conditional on an observation taken at a fixed future time-point. Such…

Computation · Statistics 2016-04-26 Gavin A. Whitaker , Andrew Golightly , Richard J. Boys , Chris Sherlock

A microscopic, driven lattice gas model is proposed for the dynamics and spatio-temporal fluctuations of the precursor film observed in spreading experiments. Matter is transported both by holes and particles, and the distribution of each…

Statistical Mechanics · Physics 2009-11-07 Douglas B. Abraham , Rodolfo Cuerno , Esteban Moro

The LHC has brought much new information on total, elastic and diffractive cross sections, which is not always in agreement with extrapolations from lower energies. The default framework in the Pythia event generator is one case in point.…

High Energy Physics - Phenomenology · Physics 2018-07-04 Christine O. Rasmussen , Torbjörn Sjöstrand

In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sampled multiple times to obtain a diverse set incurring a cost…

Machine Learning · Computer Science 2023-11-27 Gabriele Corso , Yilun Xu , Valentin de Bortoli , Regina Barzilay , Tommi Jaakkola

We propose and study a one-dimensional model which consists of two cross-diffusion systems coupled via a moving interface. The motivation stems from the modelling of complex diffusion processes in the context of the vapor deposition of thin…

Analysis of PDEs · Mathematics 2024-07-23 Clément Cancès , Jean Cauvin-Vila , Claire Chainais-Hillairet , Virginie Ehrlacher

In this paper the filtering of partially observed diffusions, with discrete-time observations, is considered. It is assumed that only biased approximations of the diffusion can be obtained, for choice of an accuracy parameter indexed by…

Computation · Statistics 2015-10-19 Ajay Jasra , Kengo Kamatani , Kody J. H. Law , Yan Zhou

Given a real valued and time-inhomogeneous martingale diffusion X, we investigate the properties of functions defined by the conditional expectation f(t,X_t)=E[g(X_T)|F_t]. We show that whenever g is monotonic or Lipschitz continuous then…

Probability · Mathematics 2008-01-03 George Lowther

Generating temporal data under conditions is crucial for forecasting, imputation, and generative tasks. Such data often has metadata and partially observed signals that jointly influence the generated values. However, existing methods face…

Machine Learning · Computer Science 2025-11-05 Aditya Shankar , Lydia Y. Chen , Arie van Deursen , Rihan Hai

In this article, we describe a simple class of models of absorbed diffusion processes with parameter, whose conditional law exhibits a transcritical bifurcation. Our proofs are based on the description of the set of quasi-stationary…

Probability · Mathematics 2021-12-13 Michel Benaïm , Nicolas Champagnat , William Oçafrain , Denis Villemonais
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