Mechanics Meet Artificial Intelligence

Machine Learning, Artificial Intelligence, and Computation in Mechanical Engineering 

“Big breakthroughs happen when what is suddenly possible meets what is desperately necessary.” 

Thomas Friedman 

New York Times, 2012 

Mechanical and Artificial Intelligence laboratory (MAIL) at CMU is inherently a multidisciplinary group bringing together researchers with different backgrounds and interests, including Mechanical, Computer Science, Bio-engineering, Physics, Material and Chemical Engineering.

Our mission is to bring the state of the art machine learning algorithm to mechanical engineering. Traditional mechanical engineering paradigms use only physics based rules and principles to model the world which does not include the intrinsic noise/stochastic nature of the system. To this end, our lab is developing the algorithms that can infer, learn and predict the mechanical systems based on data. These data-driven models incorporate the physics into learning algorithm to build more accurate predictive models. We use multi-scale simulation (CFD, MD, DFT) to generate the data.   

Mechanical engineers can use artificial intelligence (AI) in a number of ways to improve the design, analysis, and control of mechanical systems. Some examples of the applications of AI in mechanical engineering include:

Overall, AI has the potential to greatly improve the efficiency, reliability, and performance of mechanical systems, and many mechanical engineers are exploring its potential in their work.