Volume 3, Issue 2
Solving Bivariate Kinetic Equations for Polymer Diffusion Using Deep Learning

Heng Wang & Weihua Deng

J. Mach. Learn. , 3 (2024), pp. 215-244.

Published online: 2024-06

[An open-access article; the PDF is free to any online user.]

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  • Abstract

In this paper, we derive a class of backward stochastic differential equations (BSDEs) for infinite-dimensionally coupled nonlinear parabolic partial differential equations, thereby extending the deep BSDE method. In addition, we introduce a class of polymer dynamics models that accompany polymerization and depolymerization reactions, and derive the corresponding Fokker-Planck equations and Feynman-Kac equations. Due to chemical reactions, the system exhibits a Brownian yet non-Gaussian phenomenon, and the resulting equations are infinitely dimensionally coupled. We solve these equations numerically through our new deep BSDE method, and also solve a class of high-dimensional nonlinear equations, which verifies the effectiveness and shows approximation accuracy of the algorithm.

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@Article{JML-3-215, author = {Wang , Heng and Deng , Weihua}, title = {Solving Bivariate Kinetic Equations for Polymer Diffusion Using Deep Learning}, journal = {Journal of Machine Learning}, year = {2024}, volume = {3}, number = {2}, pages = {215--244}, abstract = {

In this paper, we derive a class of backward stochastic differential equations (BSDEs) for infinite-dimensionally coupled nonlinear parabolic partial differential equations, thereby extending the deep BSDE method. In addition, we introduce a class of polymer dynamics models that accompany polymerization and depolymerization reactions, and derive the corresponding Fokker-Planck equations and Feynman-Kac equations. Due to chemical reactions, the system exhibits a Brownian yet non-Gaussian phenomenon, and the resulting equations are infinitely dimensionally coupled. We solve these equations numerically through our new deep BSDE method, and also solve a class of high-dimensional nonlinear equations, which verifies the effectiveness and shows approximation accuracy of the algorithm.

}, issn = {2790-2048}, doi = {https://doi.org/10.4208/jml.240124}, url = {http://global-sci.org/intro/article_detail/jml/23212.html} }
TY - JOUR T1 - Solving Bivariate Kinetic Equations for Polymer Diffusion Using Deep Learning AU - Wang , Heng AU - Deng , Weihua JO - Journal of Machine Learning VL - 2 SP - 215 EP - 244 PY - 2024 DA - 2024/06 SN - 3 DO - http://doi.org/10.4208/jml.240124 UR - https://global-sci.org/intro/article_detail/jml/23212.html KW - BSDEs, Deep BSDE method, Polymer dynamics, Brownian yet non-Gaussian. AB -

In this paper, we derive a class of backward stochastic differential equations (BSDEs) for infinite-dimensionally coupled nonlinear parabolic partial differential equations, thereby extending the deep BSDE method. In addition, we introduce a class of polymer dynamics models that accompany polymerization and depolymerization reactions, and derive the corresponding Fokker-Planck equations and Feynman-Kac equations. Due to chemical reactions, the system exhibits a Brownian yet non-Gaussian phenomenon, and the resulting equations are infinitely dimensionally coupled. We solve these equations numerically through our new deep BSDE method, and also solve a class of high-dimensional nonlinear equations, which verifies the effectiveness and shows approximation accuracy of the algorithm.

Wang , Heng and Deng , Weihua. (2024). Solving Bivariate Kinetic Equations for Polymer Diffusion Using Deep Learning. Journal of Machine Learning. 3 (2). 215-244. doi:10.4208/jml.240124
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