Recently, Associate Professor Zhang Shuaiqi from the School of Mathematics of our university and Professor Chen Zhenqing from the University of Washington collaborated to publish two consecutive articles in SIAM Journal on Control and Optimization, one of the top three journals in the field of control. The articles, titled "Stochastic maximum principle for sub-diffusions and its application" and "Stochastic maximum principle for fully coupled forward-backward stochastic differential equations driven by subdiffusion", for the first time propose and solve the control problem of stochastic systems driven by anomalous subdiffusion, and investigate the maximum principle for subdiffusion-driven stochastic differential equations and forward-backward stochastic differential equations (FBSDEs).

Anomalous subdiffusion is a stochastic process slower than Brownian motion, characterized by unique "active" and "stagnant" phases. Zhang and his collaborators point out that these features of anomalous subdiffusion better reflect inactive market trading conditions, making them more realistic for modeling stock prices in sluggish markets. Since the system is a hybrid of deterministic and stochastic components, its control problem also involves hybrid control integrating stochastic and deterministic elements. This represents a distinctive innovation compared to existing literature.

Founded in 1966, SIAM Journal on Control and Optimization covers fields such as MATHEMATICS and APPLIED SCIENCES. It not only advances control theory, optimization methods, and their interdisciplinary applications but also provides critical theoretical support for engineering, economics, biology, and other domains. Currently, research on anomalous subdiffusion remains relatively scarce. Reviewers highly praised the articles as "noteworthy" and "impressive". This groundbreaking achievement in mathematics and control theory will provide important theoretical support for the university's digital transformation, enabling further breakthroughs in complex system optimization, intelligent decision-making, and other fields through deep integration of control theory with big data and artificial intelligence technologies.