About

Krishna Kumar

About

Krishna Kumar is an Assistant Professor at the Civil, Architecture, and Environmental Engineering at the University of Texas at Austin. Krishna completed his Ph.D. from the University of Cambridge in January 2015 on multi-scale multiphase modeling of granular flows and was supervised by Professor Kenichi Soga. Krishna’s work involves developing exascale micro and macro-scale numerical methods: Material Point Method, Lattice Boltzmann - Discrete Element coupling, Finite Element Method, and Lattice Element method. His work in high-performance computing in geomechanics provides insights into the mechanics of natural hazards such as landslides. Krishna uses Machine Learning (ML) to model multi-scale problems in geomechanics. Krishna is a Software Sustainability Institute Fellow, UK, and has developed many open-source research codes. Krishna also builds large-scale graph networks and agent-based models for simulating the resilience of city-scale infrastructure systems.

Awards and Honors

NSF CAREER Award. 2024.
Dean’s Award for Outstanding Teaching by an Assistant Professor, UT Austin, USA. 2023.
Department Teaching Award, CAEE, UT Austin, USA. 2023.
Provost Teaching Fellow, UT Austin, USA. 2023.

Media highlights

Publications

Explainable AI models for predicting liquefaction-induced lateral spreading

Hsiao, C. H, Kumar, K., Rathje, E.;

PDF: https://arxiv.org/abs/2404.15959 Dataset: https://doi.org/10.17603/ds2-6avc-1s73 Tags: xai

Modeling liquefaction-induced runout of a tailings dam using a hybrid finite element and material point method approach

Sordo, B., Rathje, E., Kumar, K.;

PDF: https://arxiv.org/abs/2404.15860 Tags: conference mpm fem

Hybrid Finite Element and Material Point Method to simulate granular column collapse from failure initiation to runout

Sordo, B., Rathje, E., Kumar, K.;

PDF: https://arxiv.org/abs/2206.07169 Tags: conference mpm fem

Inverse analysis of granular flows using differentiable graph neural network simulator

Choi, Y.J, Kumar, K.;

PDF: https://arxiv.org/abs/2401.13695 Dataset: https://doi.org/10.17603/ds2-0wjq-0j84 Tags: ml gns

Three-dimensional granular flow simulation using graph neural network-based learned simulator

Choi, Y.J, Kumar, K.;

PDF: https://arxiv.org/abs/2311.07416 Dataset: https://doi.org/10.17603/ds2-h3zz-gq43 Tags: ml gns

Graph Neural Network-based surrogate model for granular flows

Choi, Y.J, Kumar, K.;
Computers and Geotechnics

PDF: https://arxiv.org/abs/2305.05218 Tags: ml gns

Differentiable programming for inverse estimation of soil permeability and design of duct banks

Vajapeyajula, A., Kumar, K.;

PDF: http://arxiv.org/abs/2312.15293 Tags: ml diffsim

An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

Wang, Q., Kumar, K.;

PDF: https://arxiv.org/pdf/2310.00810.pdf Tags: ml sciml diff-programming

Accelerating particulate and fluid simulations with graph neural networks for solving forward and inverse problems

Kumar, K., Choi, Y.J.;

PDF: https://arxiv.org/abs/2309.13348 Tags: ml gns

Reflections on teaching engineering through murder mysteries

Kumar, K.;
Journal of Technology and Science Education

PDF: https://www.jotse.org/index.php/jotse/article/view/2014/742 Tags: teaching

Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering

Kumar, K.;

PDF: https://arxiv.org/abs/2304.02138 Tags: ml llm

Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe

Mehta, C., Kumar, K.;

PDF: https://arxiv.org/abs/2304.11273 Tags: ml llm

GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling

Kumar, K., Vantassel, J.;

PDF: https://arxiv.org/abs/2211.10228 Tags: ml gns mpm

Using explainability to design physics-aware CNNs for solving subsurface inverse problems

Crocker, J., Kumar, K., Cox, B.;

PDF: https://arxiv.org/abs/2211.08651 Tags: fwi ml cnn

Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation

Marx, D., Kumar, K., Zornberg, J.;

PDF: https://arxiv.org/abs/2212.02939 Tags: ml cnn porescale experiments

Minority Report: A graph network oracle for in situ visualization

Kumar, K., Navratil, P., Solis, A., Vantassel, J;

PDF: https://arxiv.org/abs/2206.12683 Tags: conference ml gns insitu viz

A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading

Choi, Y., Kumar, K.;

PDF: https://arxiv.org/abs/2206.07780 Tags: conference ml liquefaction

Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method

Hosseini, R., Kumar, K., Delenne, J.Y.;

PDF: https://arxiv.org/abs/2204.07174 Tags: swcc multiphase lbm porescale

Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method

Hosseini, R., Kumar, K., Delenne, J.Y.;
Powders and Grains 2021, Buenos Aires, Argentina

PDF: https://arxiv.org/abs/2102.08420 Tags: conference lbm porescale swcc hpc

Conductive and convective heat transfer in inductive heating of subsea buried pipelines

Kumar, K., El Mohtar, C., and Gilbert, R.;
ASCE Journal of Pipeline Systems Engineering and Practice

PDF: https://arxiv.org/pdf/2112.11826.pdf Tags: heat-transfer

In-situ visualization of natural hazards with Galaxy and Material Point Method

Abram, G., Solis, A., Liang, Y., and Kumar, K.;
IEEE Computing in Science & Engineering

PDF: https://arxiv.org/pdf/2109.02754 Tags: mpm insitu viz

Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes

Wang, Q., Hosseini, R., Kumar, K.;
Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, Sydney 2021

PDF: https://arxiv.org/abs/2108.12866 Tags: conference granular-flow dem lbm

Effect of Initial Volume on the Run-Out Behavior of Submerged Granular Columns

Wang, Q., Hosseini, R., Kumar, K.;
GeoCongress 2021, Dallas, USA

PDF: https://arxiv.org/abs/2011.03129 Tags: conference granular-flow dem lbm

Power electronics packaging for in-road wireless charging installations

Ridge, A., Konaklieva, S., Bradley, S., McMahon, R. A., Kumar, K.;
IEEE Workshop on Emerging Technologies - Wireless Power (WoW), San Diego, USA, 1-4 June 2021

PDF: http://wrap.warwick.ac.uk/152746/1/WRAP-Power-electronics-packaging-in-road-wireless-charging-2021.pdf Tags: conference wireless-charging heat-transfer

Biomechanical trade‑offs in the pelvic floor constrain the evolution of the human birth canal

Stansfield, E.; Kumar, K.; Mitteroecker, P.; Grunstra, N. D. S.;
Proceedings of the National Academy of Sciences of the United States of America (PNAS)

PDF: https://doi.org/10.5281/zenodo.4723996 Tags: fem biology

Using Convolutional Neural Networks (CNN) to develop starting models for 2D full waveform inversion

Vantassel, J. P.; Kumar, K.; Cox, B. R.;
Geophysics

PDF: https://arxiv.org/abs/2104.01626 Tags: ml cnn fwi

Investigating the thixotropic behaviour of tremie concrete using the slump‑flow test and the Material Point Method

Wilkes, C.; Kumar, K.; Biscontin, G.;
Concrete Rheology

PDF: https://arxiv.org/abs/2104.11792 Tags: mpm concrete-flow

Microsimulation Analysis for Network Traffic Assignment (MANTA) at Metropolitan‑Scale for Agile Transportation Planning

Yedavalli, P.; Kumar, K.; Waddell, P.;
Transportmetrica A: Transport Science

PDF: https://arxiv.org/abs/2007.03614 Tags: gpu microsim traffic-flow

Context‑specific volume‑delay curves by combining crowdsourced traffic data with Automated Traffic Counters (ATC): a case study for London

Casey, G.; Zhao, B.; Kumar, K.; Soga, K.;
Data‑Centric Engineering

PDF: https://arxiv.org/abs/2011.01964 Tags: traffic-flow