Nonlocal Model

Nonlocal models address the limitations of traditional local models by incorporating long-range interactions and dependencies, improving accuracy in diverse fields. Current research focuses on developing and applying these models using machine learning techniques, such as physics-informed neural networks and evolutionary algorithms, to solve complex problems in areas like climate modeling, material science, and traffic flow prediction. These advancements enable more accurate simulations and predictions, particularly in systems with complex, non-smooth behavior, leading to improved understanding and more effective control strategies across various scientific and engineering disciplines. The development of data-adaptive priors and efficient algorithms for handling nonlocal operators are also key areas of ongoing investigation.