The Role of AI and Machine Learning in Mechanical Engineering

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The Role of AI and Machine Learning in Mechanical Engineering

The Role of AI and Machine Learning in Mechanical EngineeringArtificial intelligence and machine learning are really ubiquitous and exciting technologies, and I really view them as another really important tool for mechanical engineers. Because, what’s the role of the mechanical engineer?

We build the physical devices you that interact with, whether or not it’s a car, it’s your Nest thermostat, it’s a medical device that your surgeon’s using. And what artificial intelligence is going to let mechanical engineers do is to take it to the next level to develop a better device, to better understand that physical phenomenon.

And I think, mechanical engineers, since we’re the physical connection: we design the products that we all interact with, we play just a central role in sort of translating that technology to make the world a better place. I think that every student should take a course in AI and machine learning, be they undergraduate or graduate.

And this is certainly true across engineering. AI and machine learning is a new tool that is not going away, and it is going to help inform engineers how to do their job better. And it’s going to be something that they need to understand because many of the tools they use will be embedded with these methods and these techniques.

So being at Carnegie Mellon, we have a lot of expertise in machine learning and artificial intelligence. And so we’re starting to look at how we can leverage that strength for fuel cells. And so, myself and Professor Burak Kara and Professor Amir Farimani in mechanical engineering: we’re looking at how to use machine learning to both improve materials design, and improve the operational control of the fuel cells and the vehicles, and improve both their performance and their durability.

My lab at CMU is focusing on using machine learning for molecular discovery, which is a pretty difficult problem because creating a functional mapping between the material geometry and topology and chemistry and basically the properties is a very difficult task. We don’t have mathematical or physical models for those.

So to that end, what we do is that we train on multiple data that are generated either via simulations or via experiments. And then we make a predictive model that, if you give me this molecule or material, what will be its properties? So we are using deep neural networks or graph convolutional neural networks in order to be able to model this functional mapping.

At Carnegie Mellon University we’re able to develop explainable, reliable, and verifiable AI products by bridging mathematics and innovation. My current projects focus on autonomous vehicles and smart cities. In the future, I hope to translate the safe AI technologies we are developing to other fields. It’s imperative that we teach our next generation of students to really learn how to leverage the data that’s acquired from these sensing systems that are really at the heart of artificial intelligence.

And this is exactly why mechanical engineering and artificial intelligence go hand-in-hand: because the systems that we will be creating in the 21st century are a blend of these. We want our students to be able to be leaders and at the cutting edge of technology.

And so they need to understand these methods as they move forward, so that they can understand how to apply them in practice. In mechanical engineering we’re infusing concepts of artificial intelligence and machine learning across our curriculum, both our undergraduate and graduate curriculum. And we’re doing this because 21st century mechanical engineers need these additional tools in their toolkit that we need to make sure all Carnegie Mellon graduates are experts in.


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