AI and machine learning for engineering design
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
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Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
A new international collaboration unites MIT and maritime industry leaders to develop nuclear propulsion technologies, alternative fuels, data-powered strategies for operation, and more.
U.S. Air Force engineer and PhD student Randall Pietersen is using AI and next-generation imaging technology to detect pavement damage and unexploded munitions.
Materials scientist is honored for his academic leadership and innovative research that bridge engineering and nature.