Researchers at Argonne are developing the deep learning framework MaLTESE (Machine Learning Tool for Engine Simulations and Experiments) to meet ever-increasing demands to deliver better engine performance, fuel economy and reduced emissions.

Utilizing ALCF supercomputing resources, Argonne researchers are developing the deep learning framework MaLTESE with autonomous -- or self-driving -- and cloud-connected vehicles in mind. This work could help meet demand to deliver better engine performance, fuel economy and reduced emissions. Researchers used nearly the full capacity of the ALCF's Theta system to simulate a typical 25-minute drive cycle of 250,000 vehicles. Researchers at Argonne are developing the deep learning framework MaLTESE (Machine Learning Tool for Engine Simulations and Experiments) to meet ever-increasing demands to deliver better engine performance, fuel economy and reduced emissions. Automotive manufacturers are facing an ever-increasing demand to deliver better engine performance, fuel economy and reduced emissions.