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The stochastic nature of chemical reactions involving randomly fluctuating population sizes has lead to a growing research interest in discrete-state stochastic models and their analysis. A widely-used approach is the description of the…

Quantitative Methods · Quantitative Biology 2015-10-01 Alexander Andreychenko , Luca Bortolussi , Ramon Grima , Philipp Thomas , Verena Wolf

In this paper we present computational techniques to investigate the solutions of two-component, nonlinear reaction-diffusion (RD) systems on arbitrary surfaces. We build on standard techniques for linear and nonlinear analysis of RD…

Computational Engineering, Finance, and Science · Computer Science 2016-05-06 Daljit Singh J. Dhillon , Michel C. Milinkovitch , Matthias Zwicker

Coupled nonlinear system of reaction-diffusion equations describing multi-component (species) interactions with heterogeneous coefficients is considered. Finite volume method based approximation for the space is used to construct…

Numerical Analysis · Mathematics 2022-09-13 Maria Vasilyeva , Alexey Sadovski , D. Palaniappan

Work presented in this paper describes a general algorithm and its finite element implementation for performing concurrent multiple sub-domain simulations in linear structural dynamics. Using this approach one can solve problems in which…

Numerical Analysis · Mathematics 2013-12-25 Tejas Ruparel , Azim Eskandarian , James Lee

The existing literature on stochastic simulation of chemical reaction networks has a tendency to move as quickly as possible to the abstract formulation of the stochastic dynamics in terms of probabilities based on the concept of the…

Statistics Theory · Mathematics 2007-06-13 Sergey Plyasunov

In synaptic molecular communication, the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process and, hence, inherently random. It is currently not fully understood how this…

Emerging Technologies · Computer Science 2023-01-18 Sebastian Lotter , Maximilian Schäfer , Robert Schober

A mesoscopic multi-particle collision model for fluid dynamics is generalized to incorporate the chemical reactions among species that may diffuse at different rates. This generalization provides a means to simulate reaction-diffusion…

Chemical Physics · Physics 2016-09-08 K. Tucci , R. Kapral

We present a brief overview of nuclear multifragmentation reaction. Basic formalism of canonical thermodynamical model based on equilibrium statistical mechanics is described. This model is used to calculate basic observables of nuclear…

Nuclear Theory · Physics 2020-09-03 G. Chaudhuri , S. Mallik , S. Das Gupta

Fractional diffusion equations (FDEs) are a mathematical tool used for describing some special diffusion phenomena arising in many different applications like porous media and computational finance. In this paper, we focus on a…

Numerical Analysis · Mathematics 2017-10-11 Hamid Moghaderi , Mehdi Dehghan , Marco Donatelli , Mariarosa Mazza

We establish the existence of solutions to a class of non-linear stochastic differential equation of reaction-diffusion type in an infinite-dimensional space, with diffusion corresponding to a given transition kernel. The solution obtained…

Probability · Mathematics 2021-08-10 Conrado da Costa , Bernardo Freitas Paulo da Costa , Daniel Valesin

We investigate robust parameter estimation and testing procedure for multivariate diffusion processes observed at high frequency via the minimum density power divergence estimator (MDPDE). Within a general diffusion framework and under…

Methodology · Statistics 2026-03-17 Sourojyoti Barick

Mean-Field Ring Polymer Molecular Dynamics (MF-RPMD) is a powerful, efficient, and accurate method for approximate quantum dynamic simulations of multi-level system dynamics. Initial efforts to compute nonadiabatic reaction rates using…

Chemical Physics · Physics 2021-04-29 Britta Ann Johnson , Nandini Ananth

Reaction diffusion equations have been used to model a wide range of biological phenomenon related to population spread and proliferation from ecology to cancer. It is commonly assumed that individuals in a population have homogeneous…

Dynamical Systems · Mathematics 2023-04-14 Kyle Nguyen , Erica M. Rutter , Kevin Flores

A common way to simulate the transport and spread of pollutants in the atmosphere is via stochastic Lagrangian dispersion models. Mathematically, these models describe turbulent transport processes with stochastic differential equations…

Reaction networks in the bulk and on surfaces are widespread in physical, chemical and biological systems. In macroscopic systems, which include large populations of reactive species, stochastic fluctuations are negligible and the reaction…

Statistical Mechanics · Physics 2007-10-12 Baruch Barzel , Ofer Biham , Raz Kupferman

Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous distribution approximation. However, their application in…

Machine Learning · Computer Science 2024-10-03 Yangming Li , Chieh-Hsin Lai , Carola-Bibiane Schönlieb , Yuki Mitsufuji , Stefano Ermon

We expand on a previous study of fronts in finite particle number reaction-diffusion systems in the presence of a reaction rate gradient in the direction of the front motion. We study the system via reaction-diffusion equations, using the…

Statistical Mechanics · Physics 2009-11-11 Elisheva Cohen , David A. Kessler , Herbert Levine

The ring-polymer molecular dynamics (RPMD) was used to calculate the thermal rate coefficients of the two-channel roaming reaction H + MgH. Both reaction channels, tight and roaming, are explicitly considered. This is a pioneering attempt…

Chemical Physics · Physics 2021-05-26 Hui Yang , Wenbin Fan , Junhua Fang , Jianing Song , Yongle Li

Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modeling. We propose MR-CDM, a framework combining hierarchical multi-resolution trend decomposition, an…

Machine Learning · Computer Science 2026-04-09 Xianyong Xu , Yuanjun Zuo , Zhihong Huang , Yihan Qin , Haoxian Xu , Leilei Du , Haotian Wang

Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data. Such mismatches are…

Machine Learning · Computer Science 2026-01-14 Matina Mahdizadeh Sani , Nima Jamali , Mohammad Jalali , Farzan Farnia
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