Rajat Arora

Rajat Arora

Senior Member of Technical Staff

Advanced Micro Devices (AMD)

Biography

Dr. Rajat Arora graduated with a Ph.D. in the field of Computational Mechanics from Carnegie Mellon University (CMU). His research at the time focused on numerical analysis, material science, and scientific software development.

Currently, Dr. Arora is a senior member of techical staff at AMD. Prior to joining AMD, he was a research scientist at Siemens Technology where his research focused on developing computational and (physics-informed) machine learning tools to accelerate scientific discovery and engineering design. Dr. Arora worked at Ansys, Inc. before joining Siemens where he made notable enhancements to the core solver of the Twin Builder product.

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Interests
  • Software Development
  • Machine Learning
  • Applied Mathematics
  • High Performance Computing
Education
  • Ph.D. in Computational Mechanics, 2019

    Carnegie Mellon University, USA

  • M.S. in Computational Mechanics, 2018

    Carnegie Mellon University, USA

  • M.Tech. in Mechanical Engineering, 2014

    Indian Institute of Technology Kanpur (IITK), India

  • B.Tech. in Mechanical Engineering, 2014

    Indian Institute of Technology Kanpur (IITK), India

Skills

Python
C++
Data Structures / Algorithm
Machine Learning
Applied Mathematics
Distributed Computing

Experience

 
 
 
 
 
Research Scientist
Aug 2020 – Present Princeton, New Jersey

Responsibilities include:

  • Developing Physics-Informed Neural networks to accelerate scientific discovery and design
  • Optimization of code for GPUs before deployment (on cloud)
  • Tools used: PyTorch, TensorFlow
 
 
 
 
 
Research & Development Engineer II
Mar 2019 – Jul 2020 Pittsburgh, PA

Responsibilities include:

  • Develop and maintain core solver for the Ansys Twinbuilder product
  • Lead developer of the Digital Twin Development framework for the Twinbuilder team
  • Tools used: C++, Python

Accomplish­ments

Coursera
Neural Networks and Deep Learning
Advanced C++ programming
See certificate

Publications

(2022). Physics-informed neural networks for modeling rate- and temperature-dependent plasticity. Submitted.

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(2021). Machine Learning-Accelerated Computational Solid Mechanics - Application to Linear Elasticity. 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE).

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(2020). Equilibrium shape of misfitting precipitates with anisotropic elasticity and anisotropic interfacial energy. Modelling and Simulation in Materials Science and Engineering.

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