Combining AI and computational science for better, faster, energy efficient predictions

Predicting how climate and the environment will change over time or how air flows over an aircraft are problems too complex even for the most powerful supercomputers to solve. Scientists rely on models to fill in the gap between what they can simulate and what they need to predict. But, as every meteorologist knows, models often rely on partial or even faulty information which may lead to bad predictions. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) are forming what they call "intelligent alloys", combining the power of computational science with artificial intelligence to develop models that complement simulations to predict the evolution of science's most complex systems. In a paper published in Nature Communications, Petros Koumoutsakos, the Herbert S. Winokur, Jr. Professor of Engineering and Applied Sciences and co-author Jane Bae, a former postdoctoral fellow at the Institute of Applied Computational Science at SEAS, combined reinforcement learning with numerical methods to compute turbulent flows, one of the most complex processes in engineering.