Hybrid Solver

Hybrid solvers combine traditional numerical methods with machine learning or quantum computing techniques to improve the efficiency and accuracy of solving complex computational problems, particularly in areas like fluid dynamics, combinatorial optimization, and partial differential equations. Current research focuses on integrating neural networks (e.g., neural operators) and quantum annealers into existing solvers, often leveraging techniques like algorithm selection and efficient data-driven prediction to accelerate computations. These advancements offer significant potential for accelerating scientific simulations and optimizing real-world applications by overcoming limitations of purely classical or purely machine learning approaches.