Predicting chaos using aerosols and AI The Source Washington University in St. Louis

If a poisonous gas were released in a bioterrorism attack, the ability to predict the path of its molecules -- through turbulent winds, temperature changes and unstable buoyancies -- could mean life or death. Understanding how a city will grow and change over a 20-year period could lead to more sustainable planning and affordable housing. Deriving equations to solve such problems -- adding up all of the relevant forces -- is, at best, difficult to the point of near-impossibility and, at worst, actually impossible. But machine learning can help. Using the motion of aerosol particles through a system in flux, researchers from the McKelvey School of Engineering at Washington University in St. Louis have devised a new model, based on a deep learning method, that can help researchers predict the behavior of chaotic systems, whether those systems are in the lab, in the pasture or anywhere else.