Researchers develop machine-learning optimizer to slash product design costs

Computer simulations are a critical part of the product design optimization process, allowing engineers to test various configurations and select the best design among the many different alternatives. But even at a facility like the U.S. Department of Energy's (DOE) Argonne National Laboratory, with its state-of-the-art resources, simulations can be very expensive and take a long time to run. With the goal of accelerating this design process, a research team in Argonne's Energy Systems (ES) division, comprised of postdoctoral appointee Opeoluwa Owoyele and research scientist Pinaki Pal, recently developed a new design optimization tool called ActivO. The new tool can drastically reduce the time needed to find the best design. It employs a novel machine learning technique that helps users focus on how to most efficiently target computational resources.