[Faculty] Fwd: [CSRC.COLLOQUIUM] "Statistics and Machine Learning in Materials Modeling"

[Jose Castillo]

[image: SDSU_CSRC Logo.jpg]





DATE:
*Friday, July 31, 2020*



TITLE:

*Statistics and Machine Learning in Materials Modeling *


TIME:
*3:00-4:00PM*




LOCATION:
Join Zoom Meeting -  *https://SDSU.zoom.us/j/96847180420*
<https://sdsu.zoom.us/j/96847180420>



SPEAKER/BIO:

*Dr. Ramin Bostanabad, Mechanical and Aerospace Engineering, University of
CA, Irvine*





ABSTRACT:

Engineered materials are an indispensable part of our modern life with
far-reaching applications that include aerial and ground transportation,
large-scale structures, national security, and medicine. The ever-evolving
societal, environmental, and cultural awareness calls for significantly
complex materials systems with unprecedented properties and functionalities
that reliably meet stakeholders’ demands under extreme conditions. The
overarching goal of my research is to statistically and mathematically
model these complexities and, in turn, accelerate the development and
deployment of engineered materials.

In this talk, I will first discuss the challenges that motivate the
research at PMACS lab which primarily include multiscale and multi-physics
nature, presence of spatiotemporally varying and coupled uncertainty
sources, lack of knowledge and computational resources, and high
dimensionality. Then, I will introduce the frameworks and methods that we
have developed to address these challenges via discipline-agnostic,
data-driven, physics-aware, and modular solutions.

Bio Sketch: Dr. Ramin Bostanabad received his Ph.D. from Northwestern
University in February 2019. He joined the Department of Mechanical and
Aerospace Engineering at UCI in September 2019 and founded the Probabilistic
Modeling and Analysis of Complex Systems (PMACS) laboratory
<https://pmacslab.eng.uci.edu/>. At PMACS lab, Dr. Bostanabad’s group
develops computational framework and tools for analyzing and designing
complex systems such as advanced manufacturing processes and multiscale
materials. These contributions are on the interface of statistics, machine
learning, and mechanics and include data-driven microstructure
characterization, multi-scale materials modeling with deep learning and
random processes, inverse system identification with hierarchical
evolutionary programming, and assimilation of multiple data sources with
Bayesian statistics.




Host: Satchi Venkataraman

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.



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