"It's not rocket science" may be a tired cliché, but that doesn't mean designing rockets is any less complicated. Time, cost and safety prohibit testing the stability of a test rocket using a physical build "trial and error" approach. But even computational simulations are extremely time consuming. A single analysis of an entire SpaceX Merlin rocket engine, for example, could take weeks, even months, for a supercomputer to provide satisfactory predictions. One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.

One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.

One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.