Parameter Space Exploration

Parameter space exploration aims to efficiently identify optimal or interesting parameter combinations within complex systems, often involving computationally expensive simulations. Current research focuses on developing surrogate models, including deep learning architectures like normalizing flows and graph neural networks, to accelerate this process and improve uncertainty quantification. These advancements enable more efficient exploration in diverse fields, from scientific modeling and tractography to optimizing autonomous systems and discovering solutions to complex equations, ultimately enhancing the speed and reliability of scientific discovery and engineering design.