Fast Solver

Fast solvers aim to efficiently find solutions to computationally expensive problems across diverse scientific domains, from trajectory optimization and wave propagation to large-scale linear programming and combinatorial optimization. Current research emphasizes integrating machine learning techniques, such as neural operators and deep neural networks, with traditional numerical methods to improve speed and accuracy, often employing iterative solvers or parallel-in-time algorithms like Parareal. These advancements are significant because they enable faster simulations and more efficient solutions for complex problems, impacting fields ranging from engineering design to AI game playing and scientific computing.