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A thermodynamic approach to rapid phase transformations within a diffuse interface in a binary system is developed. Assuming an extended set of independent thermodynamic variables formed by the union of the classic set of slow variables and…

Materials Science · Physics 2009-11-10 Peter Galenko , David Jou

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of…

The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability of N stochastic variables with Lochner's generalized Dirichlet distribution (R.H. Lochner, A Generalized…

Mathematical Physics · Physics 2013-10-02 J. Bakosi , J. R. Ristorcelli

Diffusion processes have been applied with great success to model the dynamics of large populations throughout science, in particular biology. One advantage is that they bridge two different scales: the microscopic and the macroscopic one.…

Neurons and Cognition · Quantitative Biology 2013-09-11 Marc de Kamps

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and…

Populations and Evolution · Quantitative Biology 2024-06-26 Albert C. Soewongsono , Michael J. Landis

We introduce the Fixed Point Diffusion Model (FPDM), a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Xingjian Bai , Luke Melas-Kyriazi

In this paper, we proposed a stochastic model which describes two species of particles moving in counterflow. The model generalizes the theoretical framework describing the transport in random systems since particles can work as mobile…

Soft Condensed Matter · Physics 2017-08-09 Eduardo Velasco Stock , Roberto da Silva , Henrique Almeida Fernandes

Naturally evolving proteins gradually accumulate mutations while continuing to fold to thermodynamically stable native structures. This process of neutral protein evolution is an important mode of genetic change, and forms the basis for the…

Populations and Evolution · Quantitative Biology 2007-05-23 Jesse D Bloom , Alpan Raval , Claus O Wilke

In this paper we study a class of stochastic individual-based models that describe the evolution of haploid populations where each individual is characterised by a phenotype and a genotype. The phenotype of an individual determines its…

Probability · Mathematics 2017-08-07 Martina Baar , Anton Bovier

We consider a discrete model of population with interaction where the birth and death rates are non linear functions of the population size. After proceeding to renormalization of the model parameters, we obtain in the limit of large…

Probability · Mathematics 2015-11-11 Mamadou Ba , Etienne Pardoux

Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellular responses to signals and perturbations. Although…

Molecular Networks · Quantitative Biology 2026-04-29 Suryanarayana Maddu , Victor Chardès , Michael J. Shelley

The biological theory of adaptive dynamics proposes a description of the long-term evolution of a structured asexual population. It is based on the assumptions of large population, rare mutations and small mutation steps, that lead to a…

Probability · Mathematics 2007-05-23 Nicolas Champagnat , Amaury Lambert

Recent methods for molecular generation face a trade-off: they either enforce strict equivariance with costly architectures or relax it to gain scalability and flexibility. We propose a frame-based diffusion paradigm that achieves…

Machine Learning · Computer Science 2025-10-07 Mohan Guo , Cong Liu , Patrick Forré

Designing biological sequences is an important challenge that requires satisfying complex constraints and thus is a natural problem to address with deep generative modeling. Diffusion generative models have achieved considerable success in…

Machine Learning · Computer Science 2023-06-19 Pavel Avdeyev , Chenlai Shi , Yuhao Tan , Kseniia Dudnyk , Jian Zhou

Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance on various image generation tasks, including image super-resolution. By learning to reverse the process of gradually diffusing the data distribution into…

Image and Video Processing · Electrical Eng. & Systems 2023-07-25 Kai Zhao , Alex Ling Yu Hung , Kaifeng Pang , Haoxin Zheng , Kyunghyun Sung

Fat tailed statistics and power-laws are ubiquitous in many complex systems. Usually the appearance of of a few anomalously successful individuals (bio-species, investors, websites) is interpreted as reflecting some inherent "quality"…

Statistical Mechanics · Physics 2015-05-20 Yosef E. Maruvka , David A. Kessler , Nadav M. Shnerb

Diffusion models have emerged as a powerful class of generative models, achieving state-of-the-art results in continuous data domains such as image and video generation. Their core mechanism involves a forward diffusion process that…

Computation and Language · Computer Science 2025-07-10 Ashen Weligalle

In the course of Darwinian evolution of a population, punctualism is an important phenomenon whereby long periods of genetic stasis alternate with short periods of rapid evolutionary change. This paper provides a mathematical interpretation…

Probability · Mathematics 2009-03-17 Nicolas Champagnat

Typically, aggregation-diffusion is modeled by parabolic equations that combine linear or nonlinear diffusion with a Fokker-Planck convection term. Under very general suitable assumptions, we prove that radial solutions of the evolution…

Analysis of PDEs · Mathematics 2021-12-15 Jose A. Carrillo , David Gómez-Castro , Juan Luis Vázquez

Diffusion-based generative models (DBGMs) perturb data to a target noise distribution and reverse this process to generate samples. The choice of noising process, or inference diffusion process, affects both likelihoods and sample quality.…

Machine Learning · Computer Science 2023-03-06 Raghav Singhal , Mark Goldstein , Rajesh Ranganath