Operator Splitting

Operator splitting is a mathematical technique that breaks down complex problems into smaller, more manageable subproblems, which are then solved sequentially or in parallel. Current research focuses on applying operator splitting to enhance the efficiency and convergence of various algorithms, particularly in machine learning (e.g., improving UNet architectures) and solving partial differential equations (PDEs), often within physics-informed neural networks (PINNs). This approach is proving valuable in diverse fields, accelerating computations in optimization problems, improving image segmentation techniques, and enabling faster solutions to complex scientific and engineering challenges.