Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Lu, Yubin"'
Keller-Segel systems are a set of nonlinear partial differential equations used to model chemotaxis in biology. In this paper, we propose two alternating direction implicit (ADI) schemes to solve the 2D Keller-Segel systems directly with minimal comp
Externí odkaz:
http://arxiv.org/abs/2310.20653
The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems. In this article, we propose a data-driven framework combining Reservoir Computing and Normalizing Flow to study this issue, which mimics erro
Externí odkaz:
http://arxiv.org/abs/2305.00669
This paper provide several mathematical analyses of the diffusion model in machine learning. The drift term of the backwards sampling process is represented as a conditional expectation involving the data distribution and the forward diffusion. The t
Externí odkaz:
http://arxiv.org/abs/2301.07882
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022
Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields. Moreover, a growing amount of research work tends to transfer deterministic dynamical systems to st
Externí odkaz:
http://arxiv.org/abs/2201.13114
Publikováno v:
In Materials Today Communications December 2024 41
In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties. Our contributions are, first
Externí odkaz:
http://arxiv.org/abs/2111.13164
With the rapid increase of valuable observational, experimental and simulated data for complex systems, much efforts have been devoted to identifying governing laws underlying the evolution of these systems. Despite the wide applications of non-Gauss
Externí odkaz:
http://arxiv.org/abs/2109.14881
With the rapid development of computational techniques and scientific tools, great progress of data-driven analysis has been made to extract governing laws of dynamical systems from data. Despite the wide occurrences of non-Gaussian fluctuations, the
Externí odkaz:
http://arxiv.org/abs/2108.12570
In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporally evolving probability distributions (e.g., those produced by integrat
Externí odkaz:
http://arxiv.org/abs/2107.13735
Publikováno v:
In Developmental and Comparative Immunology May 2024 154