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