Articles

2025

MLPs and KANs for data-driven learning in physical problems: A performance comparison

Pant, R., Li, S., Li, X., Iqbal, H., Kumar, K.;

PDF: https://arxiv.org/abs/2504.11397 Tags: kan

Towards scientific machine learning for granular material simulations – challenges and opportunities

Fransen, M., Fürst, A., Tunuguntla, D., Wilke, D.N., Alkin, B., Barreto, D., Brandstetter, J., Cabrera, M.A., Fan, X., Guo, M., Kieskamp, B., Kumar, K., Morrissey, J., Nuttall, J., Ooi, J., Orozco, L., Papanicolopulos, S.-A., Qu, T., Schott, D., Shuku, T., Sun, W.C., Weinhart, T., Ye, D., Cheng, H.;

PDF: https://arxiv.org/abs/2504.08766 Tags: ml

Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength

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

PDF: https://arxiv.org/abs/2503.12727 Tags: mpm fem

Investigating the Effect of CPT in Lateral Spreading Prediction Using Explainable AI

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

PDF: https://arxiv.org/abs/2503.13389 Tags: conference xai

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2024

Measuring the effective stress parameter using the multiphase lattice Boltzmann method and investigating the source of its hysteresis

Hosseini, R., Kumar, K.;

PDF: https://arxiv.org/abs/2501.00661 Tags: lbm unsaturated

A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures

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

PDF: https://arxiv.org/abs/2412.08040 Tags: mpm fem

Machine Learning Aided Modeling of Granular Materials: A Review

Wang, M., Kumar, K., Feng, Y. T., Qu, T., Wang, M.;

PDF: https://arxiv.org/abs/2410.14767 Tags: ml

Basis-to-Basis Operator Learning using Function Encoders

Ingebrand, T., Thorpe, A. J., Goswami, S., Kumar, K., Topcu, U.;

PDF: https://arxiv.org/abs/2410.00171 Tags: operator-learning

Sequential Hybrid Finite Element and Material Point Method to Simulate Slope Failures

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

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

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

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

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2023

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

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

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

Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe

Mehta, C., Kumar, K.;

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

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

Kumar, K.;

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

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2022

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

Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography

Wang, Q., Milatz, M., Hosseini, R., Kumar, K;

PDF: https://arxiv.org/abs/2209.11393 Tags: swcc lbm porescale

A frequency-velocity CNN for developing near-surface 2D Vs images from linear-array, active-source wavefield measurements

Abbas, A, Vantassel, J., Cox, B. R., Kumar, J., Crocker, J;

PDF: https://arxiv.org/abs/2207.09580 Tags: ml fwi cnn

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

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

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

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2021

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

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

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

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

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