Multiple Solution

Multiple solution research addresses the challenge of finding not just a single optimal solution to a problem, but a diverse set of near-optimal or even qualitatively different solutions. Current research focuses on developing algorithms and model architectures, including neural networks (e.g., Physics-Informed Neural Networks, and novel variations), genetic algorithms, and tree-based methods, to efficiently discover and represent these multiple solutions across various problem domains, such as differential equations, reinforcement learning, and optimization problems. This work is significant because multiple solutions offer robustness, improved understanding of problem landscapes, and enhanced adaptability in dynamic environments, impacting fields ranging from engineering design to artificial intelligence.