Physics-informed neural networks (P INNs): application categories, trends and impact

M Ghalambaz, MA Sheremet, MA Khan… - International Journal of …, 2024 - emerald.com
Purpose This study aims to explore the evolving field of physics-informed neural networks
(PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) …

Automated evolutionary approach for the design of composite machine learning pipelines

NO Nikitin, P Vychuzhanin, M Sarafanov… - Future Generation …, 2022 - Elsevier
The effectiveness of the machine learning methods for real-world tasks depends on the
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …

Partial differential equations discovery with EPDE framework: Application for real and synthetic data

M Maslyaev, A Hvatov, AV Kalyuzhnaya - Journal of Computational Science, 2021 - Elsevier
Data-driven methods provide model creation tools for systems where the application of
conventional analytical methods is restrained. The proposed method involves the data …

Predicting shallow water dynamics using echo-state networks with transfer learning

X Chen, BT Nadiga, I Timofeyev - GEM-International Journal on …, 2022 - Springer
In this paper we demonstrate that reservoir computing can be used to learn the dynamics of
the shallow-water equations. In particular, while most previous applications of reservoir …

Multi-objective discovery of pde systems using evolutionary approach

M Maslyaev, A Hvatov - 2021 IEEE Congress on Evolutionary …, 2021 - ieeexplore.ieee.org
Usually, the data-driven methods of the systems of partial differential equations (PDEs)
discovery are limited to the scenarios, when the result can be manifested as the single …

A method of generative model design based on irregular data in application to heat transfer problems

N Bykov, A Hvatov, A Kalyuzhnaya… - Journal of Physics …, 2021 - iopscience.iop.org
The paper presents the results of applying the generative design method to reconstruct a
model driven by irregular data in the form of a partial differential equation. The problem of …

[PDF][PDF] 仿真系统中DAE 求解技术现状

杨文强, 吴文渊, 陈经纬, 冯勇 - 包装工程艺术版, 2022 - chen-jingwei.github.io
目的通过对仿真系统中微分代数方程(Differential Algebraic Equations, DAE) 求解过程的描述,
协助开发人员在仿真建模求解方面有更加深入的理解, 便于分析和查找仿真建模中的问题 …

Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations

M Maslyaev, A Hvatov, A Kalyuzhnaya - Procedia Computer Science, 2020 - Elsevier
Data-driven surrogate models are widely used when the system dynamics equations and
governing models are not known a priori. The form of the differential equation with the …

Partial differential equation solver based on optimization methods

A Hvatov - arXiv preprint arXiv:2103.02294, 2021 - arxiv.org
The numerical solution methods for partial differential equation (PDE) solution allow
obtaining a discrete field that converges towards the solution if the method is applied to the …

Численное решение дифференциальных уравнений в частных производных с помощью методов оптимизации

ТА Тихонова, АА Хватов - НАУЧНЫХ РАБОТ МОЛОДЫХ УЧЁНЫХ …, 2021 - elibrary.ru
Аннотация Большинство существующих методов численного решения
дифференциальных уравнений в частных производных (ДУЧП) имеют ограничения в …