Prof. Shanjian Tang

Fudan University, China

Biography

Prof. Shanjian Tang, the School of Mathematical Sciences at Fudan University. Born in April 1966 in Wulian County, Shandong Province. He obtained his bachelor's and master's degrees from the Department of Mathematics at Shandong University in 1987 and 1990, respectively, and received his PhD from the Institute of Mathematics at Fudan University in 1993. He has served as a council member of the Chinese Society for Industrial and Applied Mathematics and as the chairman of the Mathematics Committee for Systems and Control of the same society. In 2021, he was elected as a fellow of the Chinese Society for Industrial and Applied Mathematics.His main research interests include stochastic control theory and backward stochastic differential equations. He has solved the existence and uniqueness of solutions for backward stochastic Riccati equations and established the linear quadratic optimal control theory with stochastic coefficients. 

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Abstract

Prof. Dong Shen

Renmin University of China

Biography

Dr. Dong Shen received the B.S. degree in mathematics from School of Mathematics, Shandong University, Jinan, China, in 2005, and the Ph.D. degree in mathematics from the Academy of Mathematics and System Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010. From 2010 to 2012, he was with the Institute of Automation, CAS. From 2012 to 2019, he was with the Beijing University of Chemical Technology, Beijing, China. He was a Visiting Scholar at National University of Singapore, Singapore, and RMIT University, Australia. He is a Wu Yuzhang Distinguished Professor with the School of Mathematics and the Research Center for Applied Mathematics, Renmin University of China, Beijing, China. His current research interests include iterative learning control, stochastic optimization, and distributed artificial intelligence. He has published 6 monographs and more than 200 refereed journal and conference papers. He serves on the Editorial Board of IEEE Transactions on Automatic Control, ISA Transactions, International Journal of Robust and Nonlinear Control, Journal of the Franklin Institute, and Asian Journal of Control.

Speech Title：Iterative Learning Control over Random Fading Channels

Abstract

This talk will present several recent advancements of our research group regarding the iterative learning control problem over random fading channels. First, we briefly summarize the iterative learning control method and the modeling of random fading channels, and put forward the control design and analysis problems brought about by random fading channels as a type of incomplete information. Second, we introduce relevant work from two aspects: when the statistical information of the fading channel is known and when it is unknown. In the case where the statistical information of the fading channel is known, the problems caused by the fluctuations of the input signal and the relevant results of using the average operator will be mainly introduced. In the case where the statistical information of the fading channel is unknown, the iterative estimation method and the noniterative estimation method for the unknown information will be mainly introduced.

Prof. Yang Xiang

Hong Kong University of Science and Technology, China

Biography

Professor Xiang is a Professor of the Department of Mathematics, Hong Kong University of Science and Technology. His main research directions include computational mathematics, modeling and simulation of defect problems in materials science, and machine learning theory and applications. Prof Xiang is a plenary speaker of SIAM Conference on Mathematical Aspects of Materials Science in 2021. He served as President of East Asia Section of SIAM 2023-24.

Speech Title：Modeling Effects of Randomness in High Entropy Alloys

Abstract

High entropy alloys (HEAs) are single phase crystals that consist of random solid solutions of multiple elements in approximately equal proportions. This class of novel materials have exhibited superb mechanical properties, such as high strength which is associated with the motion of dislocations (line defects). We obtain a stochastic continuum model based on the Peierls-Nabarro framework for dislocations in an HEA from an atomistic model that incorporates the atomic level randomness and short-range order. This approach provides a fundamental explanation to the origin of the high strength of HEAs based on the stochastic effects on the intrinsic strength. We also develop a machine learning framework to unravel the complex relationship between chemical composition and materials properties in HEAs.

Prof. Samad Noeiaghdam

Henan Academy of Sciences, China

Biography

Prof. Samad Noeiaghdam, PhD of Applied Mathematics, Research Professor of Henan Academy of Sciences, Zhengzhou, China and Senior Researcher of Irkutsk National Research technical University, Russia. His main research interests are numerical analysis, solving mathematical models, energy system problems, load leveling in energy storage, supply and demand systems, MHD and heat and mass transfer problems. He has published several high quality papers in top journals as well as books, chapters and conference papers. Because of his high level activities in research and contribution to mathematical advancement globally he has been acknowledged as one of the top 2% scientists by Stanford University. He is the member of editorial board and guest editor in various journals and special issues. 

Abstract: The efficient management of solar energy systems is critical to maximize energy production and ensure grid stability. This study presents a comprehensive analysis of a nonlinear system of ordinary differential equations (ODEs) that models the dynamics of energy supply, demand, and storage in a solar cell farm. The model incorporates key factors such as solar irradiance, temperature, battery storage, and maintenance costs, providing a robust framework for understanding the complex interactions within solar energy systems. Using the Adomian Decomposition Method (ADM), we solve the ODE system and analyze the stability of the equilibrium points by computing the eigenvalues of the Jacobian matrix. Real-world data from China are used to parameterize the model, ensuring practical relevance. The study evaluates the convergence of the approximate solutions and computes the residual errors for different levels of approximation, demonstrating the accuracy and reliability of the ADM. The results show that the system exhibits stable equilibrium points under normal conditions based on the real data. Also, the existence and uniqueness of solutions theorem has been proved using the Picard-Lindelof theorem.

Numerical simulations reveal that ADM provides highly accurate solutions, with residual errors decreasing significantly as the number of iterations increases. Moreover, a dynamical-numerical control has been used applying the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library. Using this method and the library, we will be able to find the optimal step of ADM, the optimal approximation, and the optimal error. This work contributes to the growing body of knowledge on solar energy systems by providing a theoretical framework for analyzing the dynamics of solar farms and offering practical insights for optimizing energy production and storage. The findings have important implications for energy policymakers, engineers, and researchers working on renewable energy systems.