Pitt | Swanson Engineering
Invited Graduate Seminar - Amir Barati Farimani (September 17, 2019)
Invited Graduate Seminar - Amir Barati Farimani (September 17, 2019)
September 17, 2019 | 11:00 AM - 12:00 PM | 102 BEH

Deep Learning of Transport Phenomena and Material Discovery

Date: 9/17/2019 Time: 11:00 AM Location: 102 BEH
Dr. Amir Barati Farimani


Convective transport has wide applications in oceanography, weather prediction, and exchange of energy and mass. In this talk, we show that modern deep learning models, such as generative adversarial networks, can be used to learn the convective transport without the knowledge of underlying constitutive equations. We will demonstrate the inference of the coupling between mass, momentum and energy. Steady-state temperature distribution and fluid flow-field are predicted with arbitrary geometric domains and boundary conditions. In contrast to conventional procedure, the deep learning models learn to generate realistic solutions in a data-driven approach and achieve state-of-the-art computational performance, while retaining high accuracy. 

In the second part, I’ll talk about different ML algorithms used in molecule/material discovery and present their accuracies for different tasks. Recently, ML shown to be very effective in material discovery and high through-put screening of materials. The limitations of these algorithm along with their interpretability will also be discussed.



Professor Farimani joined the Department of Mechanical Engineering at Carnegie Mellon University in the fall of 2018. He was previously a postdoctoral fellow at Stanford University. He received his PhD in Mechanical Engineering in 2015.


His lab at CMU focuses on the problems at the interface of Mechanical Engineering, data science and machine learning. His lab uses the state of the art deep learning and machine learning algorithms and tools to learn, infer and predict the physical phenomena pertinent to mechanical engineering. Currently, he is teaching AI and ML to a large class of graduate students at CMU.


He received the Stanley I. Weiss best thesis award from the University of Illinois in 2016 and was recognized as an Outstanding Graduate Student in 2015. During his post-doctoral fellowship at Stanford, Dr. Barati Farimani has developed data-driven, deep learning techniques for inferring, modeling, and simulating the physics of transport phenomena and for materials discovery for energy harvesting applications.


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