Linear Surrogate

Linear surrogate models are simplified representations of complex systems, aiming to efficiently approximate computationally expensive simulations or optimization problems. Current research focuses on developing and improving these surrogates using various techniques, including Bayesian optimization for optimal model parameterization, graph neural networks for handling complex 3D data, and active learning strategies to efficiently explore the solution space. These advancements are significantly impacting fields like engineering design and combinatorial optimization by accelerating simulations and enabling more efficient exploration of design options, ultimately leading to faster and more cost-effective solutions.