| A simple and flexible model order reduction method for FFT-based homogenization problems using a sparse sampling technique J Kochmann, K Manjunatha, C Gierden, S Wulfinghoff, B Svendsen, ... Computer Methods in Applied Mechanics and Engineering 347, 622-638, 2019 | 26 | 2019 |
| A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation K Manjunatha, M Behr, F Vogt, S Reese Computers in Biology and Medicine 150, 106166, 2022 | 13 | 2022 |
| A model order reduction method for finite strain FFT solvers using a compressed sensing technique C Gierden, J Kochmann, K Manjunatha, J Waimann, S Wulfinghoff, ... PAMM 19 (1), e201900037, 2019 | 4 | 2019 |
| Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation K Manjunatha, N Schaaps, M Behr, F Vogt, S Reese Computers in Biology and Medicine 167, 107686, 2023 | 3 | 2023 |
| Multi-physics modeling of in-stent restenosis K Manjunatha, M Behr, F Vogt, S Reese Proceedings of the 7th International Conference on Computational and …, 2022 | 2 | 2022 |
| Solid-tire-and-hub assembly S Purushothaman, K Manjunatha, SS Panda US Patent 10,562,351, 2020 | 2 | 2020 |
| A physics-informed deep learning framework for modeling of coronary in-stent restenosis J Shi, K Manjunatha, M Behr, F Vogt, S Reese Biomechanics and Modeling in Mechanobiology, 1-15, 2024 | 1 | 2024 |
| In silico reproduction of the pathophysiology of in-stent restenosis K Manjunatha, A Ranno, J Shi, N Schaaps, P Nilcham, A Cornelissen, ... arXiv preprint arXiv:2401.03961, 2024 | 1 | 2024 |
| Deep learning‐based surrogate modeling of coronary in‐stent restenosis J Shi, K Manjunatha, S Reese PAMM 23 (4), e202300090, 2023 | 1 | 2023 |
| In-vivo assessment of vascular injury for the prediction of in-stent restenosis A Cornelissen, RA Florescu, S Reese, M Behr, A Ranno, K Manjunatha, ... International Journal of Cardiology 388, 131151, 2023 | 1 | 2023 |
| Multiphysical Modeling of Soft Tissue-Stent Interaction S Reese Deutsche Nationalbibliothek, 2023 | 1 | 2023 |
| Finite element modelling of in-stent restenosis K Manjunatha, M Behr, F Vogt, S Reese Current Trends and Open Problems in Computational Mechanics, 305-318, 2022 | 1 | 2022 |
| In-silico Analysis of Hemodynamic Indicators in Idealized Stented Coronary Arteries for Varying Stent Indentation A Ranno, K Manjunatha, A Glitz, N Schaaps, S Reese, F Vogt, M Behr arXiv preprint arXiv:2401.08701, 2024 | | 2024 |
| Data-Driven Reduced Order Surrogate Modeling for Coronary In-Stent Restenosis (preprint) J Shi, K Manjunatha, FJ Vogt, S Reese | | 2024 |
| Development and validation of a novel in-vivo vascular injury score for prediction of in-stent restenosis A Cornelissen, RA Florescu, S Reese, M Behr, A Ranno, K Manjunatha, ... medRxiv, 2023.03. 22.23286988, 2023 | | 2023 |
| A multiphysics modeling approach for in-stent restenosis K Manjunatha, M Behr, F Vogt, S Reese | | 2022 |
| A coupled multiphysics approach for modelling in-stent restenosis M Behr Deutsche Nationalbibliothek, 2022 | | 2022 |
| S02. 04 Arteries K Manjunatha, J Frischkorn, S Reese Book of Abstracts, 119, 2020 | | 2020 |
| Data-Driven Reduced Order Surrogate Modeling for Coronary In-Stent Restenosis J Shi, K Manjunatha, FJ Vogt, S Reese Available at SSRN 4780996, 0 | | |
| Multiphysics and multiscale modeling of hemodynamics in arteries with in-stent restenosis A Ranno, K Manjunatha, F Vogt, S Reese, M Behr | | |
