Publications
* Equal contribution
2023
S. Van 't Sant*, P. Thakolkaran*, J. Martínez, S. Kumar, Inverse-designed growth-based cellular metamaterials, Mechanics of Materials, 182 (2023), 104668.
R.N. Glaesener, S. Kumar, C. Lestringant, T. Butruille, C.M. Portela, D.M. Kochmann, Predicting the influence of geometric imperfections on the mechanical response of 2D and 3D periodic trusses, Acta Materialia, 254 (2023), 118918.
M. Flaschel, S. Kumar, L. De Lorenzis, Automated discovery of generalized standard material models with EUCLID, Computer Methods in Applied Mechanics and Engineering, 405 (2023) 115867.
E. Marino, M. Flaschel , S. Kumar, L. De Lorenzis, Automated identification of linear viscoelastic constitutive laws with EUCLID, Mechanics of Materials, 181 (2023), 104643.
J. Tang, S. Kumar, L. De Lorenzis, E. Hosseini, Neural cellular automata for solidification microstructure modelling, Arxiv:2304.02354.
J. Boddapati, M. Flaschel, S. Kumar, L. De Lorenzis, C. Daraio, Single-test evaluation of directional elastic properties of anisotropic structured materials, Arxiv:2304.09112.
2022
P. Thakolkaran, A. Joshi, Y. Zheng, M. Flaschel, L. De Lorenzis, S. Kumar, NN-EUCLID: deep-learning hyperelasticity without stress data, Journal of the Mechanics and Physics of Solids, 169 (2022) 105076.
A. Joshi, P. Thakolkaran, Y. Zheng, M. Escande, M. Flaschel, L. De Lorenzis, S. Kumar, Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties, Computer Methods in Applied Mechanics and Engineering, 398 (2022) 115225.
M. Flaschel, S. Kumar, L. De Lorenzis, Discovering plasticity models without stress data, npj Computational Materials, 8 (2022), 91.
J. H. Bastek, S. Kumar, B. Telgen, R. N. Glaesener, D. M. Kochmann, Inverting the structure–property map of truss metamaterials by deep learning, Proceedings of the National Academy of Sciences, 119 (1) e2111505119 (2022).
H. C. Hille, S. Kumar, L. De Lorenzis, Floating Isogeometric Analysis, Computer Methods in Applied Mechanics and Engineering, 392 (2022), 114684.
S. Kumar, D. M. Kochmann, What machine learning can do for computational solid mechanics, Current Trends and Open Problems in Computational Mechanics, Springer (2022). [Book chapter]
J. Voss, R. J. Martin, O. Sander, S. Kumar, D. M. Kochmann, P. Neff, Numerical approaches for investigating quasiconvexity in the context of Morrey's conjecture, Journal of Nonlinear Science, 32, 77 (2022).
2021
L. Zheng, S. Kumar, D. M. Kochmann, Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy, Computer Methods in Applied Mechanics and Engineering, 383 (2021), 113894.
M. Flaschel*, S. Kumar*, L. De Lorenzis, Unsupervised discovery of interpretable hyperelastic constitutive laws, Computer Methods in Applied Mechanics and Engineering, 381 (2021), 113852.
2020
S. Kumar, S. Tan, L. Zheng, D. M. Kochmann, Inverse-designed spinodoid metamaterials, npj Computational Materials, 6 (2020), 73.
S. Kumar, A. Vidyasagar, D. M. Kochmann, An assessment of numerical techniques to find energy‐minimizing microstructures associated with nonconvex potentials, International Journal for Numerical Methods in Engineering, 121 (2020), 1595-1628 (cover article).
S. Kumar*, A. D. Tutcuoglu*, Y. Hollenweger, D. M. Kochmann, A meshless multiscale approach to modeling severe plastic deformation of metals: Application to ECAE of pure copper, Computational Material Science, 173 (2020), 109329.
2019
S. Kumar, K. Danas, D. M. Kochmann, Enhanced local maximum-entropy approximation for stable meshfree simulations, Computer Methods in Applied Mechanics and Engineering, 344 (2019), 858-886.
Before 2017
R. Singh, S. Kumar, A. Kumar, Effect of intrinsic twist and orthotropy on extension-twist-inflation coupling in compressible circular tubes, Journal of Elasticity, 128 (2017), 175-201
A. Kumar, S. Kumar, P. Gupta, A Helical Cauchy-Born rule for special Cosserat rod modeling of nano and continuum rods, Journal of Elasticity, 124 (2016), 81-106
Thesis:
S. Kumar, An enhanced maximum-entropy-based meshfree method: theory and applications, PhD Thesis, California Institute of Technology (2019)