Prashant K. Jha
Scholar

Prashant K. Jha

Google Scholar ID: XvQBKlwAAAAJ
Assistant Professor, Mechanical Engineering, South Dakota School of Mines and Technology, Rapid City
computational mechanicsmathematical modeling
Citations & Impact
All-time
Citations
513
 
H-index
13
 
i10-index
16
 
Publications
20
 
Co-authors
0
 
Resume (English only)
Academic Achievements
  • - Associate Editor for the Journal of Peridynamics and Nonlocal Modeling
  • - Topic Editor for the Journal of Open Source Software (JOSS)
  • - Editorial Board Member of Scientific Reports
  • - NSF research grant awarded in 2025 for developing an Adaptive Multi-Fidelity Framework for Modeling Heterogeneous Materials Under Extreme Conditions
  • - SDBOR Competitive Research Grant (CRG) award in 2025 for modeling and design of magnetic soft materials
  • - Published multiple papers on topics such as uncertainty quantification, optimal experimental design, neural operators, and finite element methods
Research Experience
  • - Postdoctoral Fellow in the Department of Mathematics at Louisiana State University from 2016 to 2018, working on numerical methods and analysis of peridynamics theory of fracture.
  • - Worked at the Oden Institute at UT Austin, focusing on computational mechanics of multiphysics and complex systems.
Education
  • - PhD in Civil and Environmental Engineering, 2016, Carnegie Mellon University, Pittsburgh, USA
  • - ME in Mechanical Engineering, 2012, Indian Institute of Science, Bangalore, India
  • - BE in Mechanical Engineering, 2010, Govt. Engineering College, Raipur, India
Background
  • Assistant Professor in the Leslie A. Rose Department of Mechanical Engineering at the South Dakota School of Mines and Technology. Research interests include mechanics of solids and granular media, fracture mechanics, multiphysics and multiscale modeling, and applications of neural networks to engineering problems.
Miscellany
  • Interests include fracture mechanics, mechanics of solids and granular media, multiphysics and multiscale modeling, scientific machine learning, and uncertainty quantification
Co-authors
0 total
Co-authors: 0 (list not available)