Machine learning paves way for rapid rocket engine design

"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. Scientific machine learning is a relatively new field that blends scientific computing with machine learning. Through a combination of physics modeling and data-driven learning, it becomes possible to create reduced-order models – simulations that can run in a fraction of the time, making them particularly useful in the design setting.


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