Prashant K. Jha

Prashant K. Jha

Research Associate

The University of Texas at Austin


I am a Research Associate at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. I received a Ph.D. in Civil and Environmental Engineering from Carnegie Mellon University in August 2016. After finishing my Ph.D., I joined the Department of Mathematics at Louisiana State University as a Postdoctoral Fellow and worked on numerical methods and analysis of the peridynamics theory of fracture. I moved to UT Austin in August 2019 to further expand my expertise and knowledge in computational mechanics. My research interests include solids and granular media mechanics, fracture mechanics, multiphysics and multiscale modeling, and applications of neural networks to engineering problems.

In Fall 2021, I taught two undergraduate courses on numerical methods. I am serving the Journal of Peridynamics and Nonlocal Modeling as one of the associate editors and the Journal of Open Source Software (JOSS) as topic editor. Recently, I joined the editorial team of Scientific Reports as an Editorial Board Member.


  • Fracture Mechanics
  • Mechanics of Solids and Granular Media
  • Multiphysics and Multiscale Modeling
  • Scientific Machine Learning
  • Uncertainty Quantification


  • 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



Research Associate

The University of Texas at Austin

Nov 2020 – Present Austin

Postdoctoral Scholar

The University of Texas at Austin

Aug 2019 – Nov 2020 Austin

Postdoctoral Scholar

Louisiana State University

Oct 2016 – Jul 2019 Baton Rouge

Professional Responsibilities

1. Associate Editor

Journal of Peridynamics and Nonlocal Modeling (JPER)

2. Topic Editor

Journal of Open Source Software (JOSS)

3. Editorial Board Member

Scientific Reports

4. Journal Reviews

CMAME, JMPS, SINUM, M3AS, JALCOM, PHYSA, Mathematical Reviews (AMS)


A Mechanistic Tumor Growth Model for HP MRI

With David Fuentes at MD Anderson Cancer Center, Houston, USA. Awarded under the MDACC-Oden-TACC joint initiative. $50k (Sep 2020 – Aug 2021)

Recent Publications

See all publications

(2023). Discrete-to-Continuum Limits of Long-Range Electrical Interactions in Nanostructures. Archive for Rational Mechanics and Analysis.

PDF Source Document DOI

(2023). Residual-Based Error Correction for Neural Operator Accelerated Infinite-Dimensional Bayesian Inverse Problems. Journal of Computational Physics.

PDF Source Document DOI

(2022). Goal-Oriented A-Posteriori Estimation of Model Error as an Aid to Parameter Estimation. Journal of Computational Physics.

PDF Source Document DOI

(2022). Atomic-to-Continuum Multiscale Modeling of Defects in Crystals with Nonlocal Electrostatic Interactions. Journal of Applied Mechanics.

PDF Source Document DOI


See all projects


Neural Networks to Accelerate Scientific Computing

Development and application of neural networks to accelerate scientific computing in areas of mechanistic simulation, parameter estimation, model selection, and optimization of materials and structures.

Computational methods for nonlocal models

Develop efficient and parallel computational methods for class of nonlocal models such as Peridynamics and nonlocal diffusion equations

Analysis and application of peridynamics

Analysis and application of peridynamics

Modeling tumor growth, angiogenesis, drug-therapy, metastasis

Development and analysis of models of tumor growth, angiogenesis, drug therapy, and metastasis

Study of granular media

Study properties of granular media using computational methods


See complete list



3D-1D tumor growth model for simulation of angiogenesis


Peridynamics-based discrete element method for granular media


Nonlocal mechanics library for peridynamics simulation


Calibration of SEIRD model under uncertainty: Application of Bayesian statistics


Implementation of a distributed nonlocal heat equation solver with load balancing