MIT Sloan Management Review Article on How Human-Informed AI Leads to More Accurate Digital Twins

A hybrid approach that combines human knowledge and machine learning delivers better results for mission-critical simulations.

“OK, Houston, we’ve had a problem here,” said astronaut John Swigert in 1970, after an explosion disabled the Apollo 13 spacecraft on its way to the moon, 200,000 miles from Earth. Those iconic words triggered a heroic effort that eventually succeeded in bringing the astronauts home safely. To do so, NASA scientists and engineers needed to develop and test innovative solutions on the fly. A critical tool they used was an Earth-based “twin” of the spacecraft — then, mostly physical — upon which they could experiment swiftly and safely without endangering the astronauts.

Half a century later, this concept has evolved into the digital twin (DT) — a digital replica of a complex real-world entity. DTs comprise two key elements: a high-fidelity model of the entity and a dynamic mechanism to keep the model true in real time, even as the entity undergoes changes. In industrial settings, internet-of-things (IoT) sensors typically provide the data for dynamic updates. DTs are especially powerful tools for mission-critical applications, where experimenting with the physical system isn’t feasible or is expensive, time-consuming, and hazardous.

About the Author

Pushkar P. Apte is global lead for the Smart Data-AI Initiative and strategic technology advisor, SEMI. Costas J. Spanos is Andrew S. Grove Distinguished Professor Emeritus, EECS, at UC Berkeley.

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