Mathematical Model

Mathematical modeling involves representing real-world phenomena using mathematical equations and algorithms to analyze and predict behavior. Current research focuses on developing and applying diverse model architectures, including artificial neural networks, compartmental models, and physics-informed neural networks, to address challenges in diverse fields such as astrophysics, robotics, epidemiology, and biology. These models are used for tasks ranging from predicting disease spread and optimizing industrial processes to analyzing complex biological systems and improving AI systems. The resulting insights and predictions have significant implications for advancing scientific understanding and informing practical decision-making across numerous disciplines.