Learning Based Solver

Learning-based solvers aim to leverage the power of machine learning to efficiently solve complex optimization and differential equation problems, often outperforming traditional methods, especially for large-scale instances. Current research focuses on developing novel architectures like graph neural networks, deep unfolding networks, and autoencoders, often combined with techniques such as continuous relaxation, variational inference, and active learning to improve speed, accuracy, and generalization capabilities. These advancements hold significant promise for accelerating scientific simulations across diverse fields, from fluid dynamics and materials science to power systems optimization and combinatorial problems, ultimately enabling faster and more efficient problem-solving.