A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster
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MIT researchers developed a computational approach that can be used to solve problems with hundreds of variables. In tests on realistic engineering challenges, the approach found top solutions 10 to 100 times faster than methods such as Bayesian optimization.
The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design. Adam Zewe | MIT News Publication Date: March 4, 2026 Press Inquiries Press Contact: Melanie Grados Email: mgrados@mit.edu Phone: 617-253-1682 MIT News Office Media Download ↓ Download Image Caption: “A car might have 300 design criteria, but not all of them are the main driver of the best design if you are trying to increase some safety parameters. Our algorithm can smartly select the most critical features to focus on,” Rosen Yu says. Credits: Image: MIT News; iStock *Terms of Use: Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a Creative Commons Attribution Non-Commercial No Derivatives license. You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT." Close Caption: “A car might have 300 design criteria, but not all of them are the main driver of the best design if you are trying to increase some safety parameters. Our algorithm can smartly select the most critical features to focus on,” Rosen Yu says. Credits: Image: MIT News; iStock Previous image Next image Many engineering challenges come down to the same headache — too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each eval...