| Unsupervised discovery of interpretable hyperelastic constitutive laws M Flaschel, S Kumar, L De Lorenzis Computer Methods in Applied Mechanics and Engineering 381, 113852, 2021 | 162 | 2021 |
| 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 | 90 | 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 | 72 | 2023 |
| Discovering plasticity models without stress data M Flaschel, S Kumar, L De Lorenzis npj Computational Materials 8 (1), 91, 2022 | 65* | 2022 |
| 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 | 46 | 2022 |
| Automated identification of linear viscoelastic constitutive laws with EUCLID E Marino, M Flaschel, S Kumar, L De Lorenzis Mechanics of Materials 181, 104643, 2023 | 25 | 2023 |
| A review on data-driven constitutive laws for solids JN Fuhg, G Anantha Padmanabha, N Bouklas, B Bahmani, WC Sun, ... Archives of Computational Methods in Engineering, 1-43, 2024 | 11 | 2024 |
| 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 | 10 | 2023 |
| Single-test evaluation of directional elastic properties of anisotropic structured materials J Boddapati, M Flaschel, S Kumar, L De Lorenzis, C Daraio Journal of the Mechanics and Physics of Solids 181, 105471, 2023 | 8 | 2023 |
| Reduced and all-at-once approaches for model calibration and discovery in computational solid mechanics U Römer, S Hartmann, JA Tröger, D Anton, H Wessels, M Flaschel, ... Applied Mechanics Reviews, 1-51, 2024 | 7 | 2024 |
| Automated Discovery of Material Models in Continuum Solid Mechanics M Flaschel ETH Zurich, 2023 | 7 | 2023 |
| Supplementary software for “Discovering plasticity models without stress data" M Flaschel, S Kumar, L De Lorenzis ETH Lib, 2022 | 5 | 2022 |
| FEM Data - Unsupervised discovery of interpretable hyperelastic constitutive laws M Flaschel, S Kumar, L De Lorenzis ETH Zurich, 2021 | 5 | 2021 |
| FEM Data - Discovering plasticity models without stress data M Flaschel, S Kumar, L De Lorenzis ETH Zurich, 2022 | 4 | 2022 |
| Calibration of material parameters based on 180° and 90° ferroelectric domain wall properties in Ginzburg–Landau–Devonshire phase field models M Flaschel, L De Lorenzis Archive of Applied Mechanics 90 (12), 2755–2774, 2020 | 3 | 2020 |
| Discovering non-associated pressure-sensitive plasticity models with EUCLID H Xu, M Flaschel, L De Lorenzis | 1 | 2024 |
| FEM Data-Automated discovery of generalized standard material models with EUCLID M Flaschel, S Kumar, L De Lorenzis ETH Zurich, 2022 | 1 | 2022 |
| Data‐driven methods for quantitative imaging G Dong, M Flaschel, M Hintermüller, K Papafitsoros, C Sirotenko, ... GAMM‐Mitteilungen, e202470014, 2024 | | 2024 |
| Data-driven Regularization and Quantitative Imaging M Flaschel, M Hintermüller, C Sirotenko, K Tabelow Annual Research Report 2023, 2023 | | 2023 |
| Convex Neural Networks Learn Generalized Standard Material Models M Flaschel, P Steinmann, L De Lorenzis, E Kuhl Available at SSRN 5023581, 0 | | |
Laura De LorenzisETH ZürichEmail yang diverifikasi di ethz.ch