| Inverse-designed spinodoid metamaterials S Kumar, S Tan, L Zheng, DM Kochmann npj Computational Materials 6 (1), 73, 2020 | 235 | 2020 |
| Unsupervised discovery of interpretable hyperelastic constitutive laws M Flaschel, S Kumar, L De Lorenzis Computer Methods in Applied Mechanics and Engineering 381, 113852, 2021 | 140 | 2021 |
| Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy L Zheng, S Kumar, DM Kochmann Computer Methods in Applied Mechanics and Engineering 383, 113894, 2021 | 130 | 2021 |
| Inverting the structure–property map of truss metamaterials by deep learning JH Bastek, S Kumar, B Telgen, RN Glaesener, DM Kochmann Proceedings of the National Academy of Sciences 119 (1), e2111505119, 2022 | 126 | 2022 |
| NN-EUCLID: Deep-learning hyperelasticity without stress data P Thakolkaran, A Joshi, Y Zheng, M Flaschel, L De Lorenzis, S Kumar Journal of the Mechanics and Physics of Solids 169, 105076, 2022 | 71 | 2022 |
| Automated discovery of generalized standard material models with EUCLID M Flaschel, S Kumar, L De Lorenzis Computer Methods in Applied Mechanics and Engineering 405, 115867, 2023 | 64 | 2023 |
| Discovering plasticity models without stress data M Flaschel, S Kumar, L De Lorenzis npj Computational Materials 8 (1), 91, 2022 | 53 | 2022 |
| What machine learning can do for computational solid mechanics S Kumar, DM Kochmann Current Trends and Open Problems in Computational Mechanics, 275-285, 2022 | 46 | 2022 |
| Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling L Zheng, K Karapiperis, S Kumar, DM Kochmann Nature Communications 14 (1), 7563, 2023 | 40 | 2023 |
| Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties A Joshi, P Thakolkaran, Y Zheng, M Escande, M Flaschel, L De Lorenzis, ... Computer Methods in Applied Mechanics and Engineering 398, 115225, 2022 | 38 | 2022 |
| A helical Cauchy-Born rule for special Cosserat rod modeling of nano and continuum rods A Kumar, S Kumar, P Gupta Journal of Elasticity 124, 81-106, 2016 | 35 | 2016 |
| Enhanced local maximum-entropy approximation for stable meshfree simulations S Kumar, K Danas, DM Kochmann Computer Methods in Applied Mechanics and Engineering 344, 858-886, 2019 | 29 | 2019 |
| Automated identification of linear viscoelastic constitutive laws with EUCLID E Marino, M Flaschel, S Kumar, L De Lorenzis Mechanics of Materials, 104643, 2023 | 25 | 2023 |
| Inverse-designed growth-based cellular metamaterials S Van’t Sant, P Thakolkaran, J Martínez, S Kumar Mechanics of Materials 182, 104668, 2023 | 19 | 2023 |
| An assessment of numerical techniques to find energy‐minimizing microstructures associated with nonconvex potentials S Kumar, A Vidyasagar, DM Kochmann International Journal for Numerical Methods in Engineering 121 (7), 1595-1628, 2020 | 19 | 2020 |
| Effect of intrinsic twist and orthotropy on extension–twist–inflation coupling in compressible circular tubes R Singh, S Kumar, A Kumar Journal of Elasticity 128, 175-201, 2017 | 18 | 2017 |
| A meshless multiscale approach to modeling severe plastic deformation of metals: Application to ECAE of pure copper S Kumar, AD Tutcuoglu, Y Hollenweger, DM Kochmann Computational Materials Science 173, 109329, 2020 | 13 | 2020 |
| Numerical approaches for investigating quasiconvexity in the context of Morrey’s conjecture J Voss, RJ Martin, O Sander, S Kumar, DM Kochmann, P Neff Journal of Nonlinear Science 32 (6), 77, 2022 | 10 | 2022 |
| Predicting the influence of geometric imperfections on the mechanical response of 2D and 3D periodic trusses RN Glaesener, S Kumar, C Lestringant, T Butruille, CM Portela, ... Acta Materialia 254, 118918, 2023 | 9 | 2023 |
| Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID M Flaschel, H Yu, N Reiter, J Hinrichsen, S Budday, P Steinmann, ... Journal of the Mechanics and Physics of Solids 180, 105404, 2023 | 7 | 2023 |
Dennis KochmannETH ZurichVerified email at ethz.ch
