| FE: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining KA Kalina, L Linden, J Brummund, M Kästner Computational Mechanics 71 (5), 827-851, 2023 | 40 | 2023 |
| Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks KA Kalina, L Linden, J Brummund, P Metsch, M Kästner Computational Mechanics 69 (1), 213-232, 2022 | 40 | 2022 |
| Neural networks meet hyperelasticity: A guide to enforcing physics L Linden, DK Klein, KA Kalina, J Brummund, O Weeger, M Kästner Journal of the Mechanics and Physics of Solids 179, 105363, 2023 | 38 | 2023 |
| Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria KA Kalina, P Gebhart, J Brummund, L Linden, WC Sun, M Kästner Computer Methods in Applied Mechanics and Engineering 421, 116739, 2024 | 6 | 2024 |
| Thermodynamically consistent constitutive modeling of isotropic hyperelasticity based on artificial neural networks L Linden, KA Kalina, J Brummund, P Metsch, M Kästner PAMM 21 (1), e202100144, 2021 | 3 | 2021 |
| 4.4 Neural networks meet hyperelasticity: On limits of polyconvexity DK Klein, L Linden, KA Kalina, R Ortigosa, M Kästner, O Weeger 11th GAMM AG Data Workshop TU Dresden February 06/07, 2024, 12, 2024 | | 2024 |
| 4.2 Automated constitutive modeling of hyperelastic solids based on physics-augmented neural networks L Linden, KA Kalina, J Brummund, M Kästner 11th GAMM AG Data Workshop TU Dresden February 06/07, 2024, 10, 2024 | | 2024 |
| Homogenized data magneto-active polymers KA Kalina, P Gebhart, J Brummund, L Linden, WC Sun, M Kästner Technische Universität Dresden, 2023 | | 2023 |
Karl A. KalinaInstitute of Solid Mechanics, TU DresdenVerified email at tu-dresden.de
