Benjamin A. Jasperson

Los Angeles, CA

I am a Postdoctoral Scholar - Research Associate in Professor Krishna Garikipati’s group at the University of Southern California. I recently received my Ph.D. in Theoretical and Applied Mechanics from the University of Illinois Urbana-Champaign (advisor: Professor Harley Johnson). I was also a member of the DIGI-MAT program.

My research focuses on optimizing designs and computational models to solve mechanics-based problems. My recent projects include work in topology optimization as well as atomistic methods (interatomic modeling). I’m especially interested in using data-driven methods and machine learning in solving these problems. Although my current research is focused on computational methods, I did experimental research (design, fabrication and testing) during my Master’s degree program.

I received my Bachelor’s and Master’s degrees in mechanical engineering from the University of Wisconsin - Madison (advisors: Professor Frank Pfefferkorn and Professor Kevin Turner). I also worked for ~10 years as a design engineer on products ranging from healthcare devices to neutron generators. I am a Professional Engineer (P.E.) in the State of Wisconsin.

news

May 28, 2024	If you are heading to EMI/PMC 2024 in Chicago this week, please stop by the Buckingham Room on Wednesday, May 29 at 11:30am. I will be presenting our recent work, as seen in MRS Advances. As a part of the talk, I will describe how we transitioned our dual neural network topology optimization algorithm to aid in fitting interatomic potentials. This initial proof-of-concept work could eventually be used to aid in efficiently fitting an interatomic potential for large scale properties of interest, e.g. grain boundary energy and plastic flow strength.
Apr 4, 2024	I’m excited to share our recent article, which extends our neural-network based optimization framework to interatomic potentials. You can find the article, which was recently published in MRS-Advances, here
Mar 8, 2024	Our recent work with Michael Wood was highlighted in the Sandia National Laboratories Collaboration Report for 2022-2023. You can see the article here, pg 58-59. I have truly enjoyed being a part of this great collaboration!
Dec 9, 2023	I’m excited to share our work using neural networks to perform topology optimization. You can find the article, which was recently published in Computer-Aided Design, here
Jul 28, 2023	Thank you to the organizers at USNCCM17, where I just presented our topology optimization work. It was great to meet everyone and hear about all of your research. I’m looking forward to USNCCM18 in Chicago!

selected publications

Cross-Scale Covariance for Material Property Prediction

Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, and 4 more authors

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A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (\∼10^8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (\≤10^2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to quantify uncertainty over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the uncertainty bounds established in our statistical study.
A Dual Neural Network Approach to Topology Optimization for Thermal-Electromagnetic Device Design

Benjamin A. Jasperson, Michael G. Wood, and Harley T. Johnson

Computer-Aided Design, Dec 2023

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Thin film heat flux sensors fabricated on copper substrates for thermal measurements in microfluidic environments

Benjamin A Jasperson, Joshua Schmale, Weilin Qu, and 2 more authors

Journal of Micromechanics and Microengineering, Dec 2014

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