Search

Your search keyword '"Second order stochastic differential equations"' showing total 4 results
Start Over Descriptor "Second order stochastic differential equations"
4 results on '"Second order stochastic differential equations"'

1. Exact solutions of stochastic Burgers–Korteweg de Vries type equation with variable coefficients

Author

Kolade Adjibi, Allan Martinez, Miguel Mascorro, Carlos Montes, Tamer Oraby, Rita Sandoval, and Erwin Suazo

Subjects
Stochastic Burgers–KdV equation, Second order stochastic differential equations, Stochastic mesh, Applied mathematics. Quantitative methods, T57-57.97
Abstract

We will present exact solutions for three variations of the stochastic Korteweg de Vries–Burgers (KdV–Burgers) equation featuring variable coefficients. In each variant, white noise exhibits spatial uniformity, and the three categories include additive, multiplicative, and advection noise. Across all cases, the coefficients are time-dependent functions. Our discovery indicates that solving certain deterministic counterparts of KdV–Burgers equations and composing the solution with a solution of stochastic differential equations leads to the exact solution of the stochastic Korteweg de Vries–Burgers (KdV–Burgers) equations.

Published
2024
Full Text
View/download PDF

2. Exact and numerical solution of stochastic Burgers equations with variable coefficients.

Author

Flores, Stephanie, Hight, Elijah, Olivares-Vargas, Everardo, Oraby, Tamer, Palacio, Jose, Suazo, Erwin, and Yoon, Jasang

Subjects
BURGERS' equation, STOCHASTIC differential equations, DIFFERENTIAL equations, HAMBURGERS, STOCHASTIC orders
Abstract

We will introduce exact and numerical solutions to some stochastic Burgers equations with variable coefficients. The solutions are found using a coupled system of deterministic Burgers equations and stochastic differential equations. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

4. Building General Langevin Models from Discrete Datasets

Author

Federica Ferretti, Aleksandra M. Walczak, Thierry Mora, Victor Chardès, Irene Giardina, Physique Statistique et Inférence pour la Biologie, Laboratoire de physique de l'ENS - ENS Paris (LPENS), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Laboratoire de physique de l'ENS - ENS Paris (LPENS (UMR_8023)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)

Subjects
Discretization, Computer science, QC1-999, [SDV]Life Sciences [q-bio], Degrees of freedom (statistics), Complex system, FOS: Physical sciences, General Physics and Astronomy, Inference, Bayesian inference, Quantitative Biology - Quantitative Methods, 01 natural sciences, 010305 fluids & plasmas, Hamiltonian system, 0103 physical sciences, Statistical physics, 010306 general physics, Langevin dynamics, Condensed Matter - Statistical Mechanics, Quantitative Methods (q-bio.QM), [PHYS]Physics [physics], Statistical Mechanics (cond-mat.stat-mech), Physics, Estimator, second order stochastic differential equations, non Markovian dynamics, maximum likelihood estimators, state space models, Physics - Data Analysis, Statistics and Probability, FOS: Biological sciences, Data Analysis, Statistics and Probability (physics.data-an)
Abstract

Many living and complex systems exhibit second order emergent dynamics. Limited experimental access to the configurational degrees of freedom results in data that appears to be generated by a non-Markovian process. This poses a challenge in the quantitative reconstruction of the model from experimental data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian systems. We develop a novel Bayesian inference approach to learn the parameters of such stochastic effective models from discrete finite length trajectories. We first discuss the failure of naive inference approaches based on the estimation of derivatives through finite differences, regardless of the time resolution and the length of the sampled trajectories. We then derive, adopting higher order discretization schemes, maximum likelihood estimators for the model parameters that provide excellent results even with moderately long trajectories. We apply our method to second order models of collective motion and show that our results also hold in the presence of interactions., Comment: we correct previous inaccuracy about a reference; 29 pages, 9 figures

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results