Automatic Solver

Automatic solvers are computational tools designed to efficiently find solutions to complex problems across diverse domains, ranging from mathematical equations and power grid optimization to robotic path planning and program synthesis. Current research emphasizes developing more efficient and robust solvers, often integrating machine learning techniques like neural networks (e.g., graph neural networks, transformers) and reinforcement learning to improve performance and generalization across problem instances. These advancements are significant because they enable faster and more accurate solutions to computationally intensive problems, impacting fields like energy systems, computer-aided design, and artificial intelligence.