AI tool from MIT speeds up complex engineering optimisation
The system speeds up advanced engineering design by pinpointing key variables, achieving top solutions up to 100 times faster than traditional methods.
MIT researchers have developed a new AI approach that helps engineers solve complex design problems faster, from power grid optimisation to vehicle safety.
The method adapts a foundation model trained on tabular data, enabling high-dimensional optimisation without retraining and significantly speeding up results.
The system uses a foundation model with Bayesian optimisation to pinpoint the variables that most impact outcomes. Focusing on key variables, the model finds top solutions 10 to 100 times faster than existing optimisation methods.
Early tests show the approach excels in costly, time-consuming scenarios like car crash testing and power system design. The technique lowers computational demands and suits large-scale, high-frequency engineering challenges across multiple domains.
Researchers aim to expand the method to even higher-dimensional problems, such as naval ship design, while highlighting the broader potential of foundation models as algorithmic engines in scientific and engineering tools.
Experts see it as a practical step toward making advanced optimisation more accessible in real-world applications.
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