| Universal differential equations for scientific machine learning C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385, 2020 | 824 | 2020 |
| Model Simplification For Dynamic Control of Series-Parallel Hybrid Robots - A Representative Study on the Effects of Neglected Dynamics FK Shivesh Kumar, Julius Martensen, Andreas Mueller IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2019), 2019 | 13 | 2019 |
| Universal differential equations for scientific machine learning. ArXiv preprint abs/2001.04385 (2020) C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... | 12 | 2001 |
| Selection of decoupling control methods suited for automated design for uncertain TITO processes M Noeding, J Martensen, N Lemke, W Tegethoff, J Koehler 2018 IEEE 14th International Conference on Control and Automation (ICCA …, 2018 | 11 | 2018 |
| Towards Machine Learning of Power-2-Methanol Processes CJ Martensen, C Plate, T Keßler, C Kunde, L Kaps, A Kienle, ... Computer Aided Chemical Engineering 52, 561-568, 2023 | 3 | 2023 |
| Symbolic-numeric integration of univariate expressions based on sparse regression S Iravanian, CJ Martensen, A Cheli, S Gowda, A Jain, Y Ma, ... ACM Communications in Computer Algebra 56 (2), 84-87, 2022 | 3 | 2022 |
| Optimal experimental design for universal differential equations C Plate, CJ Martensen, S Sager arXiv preprint arXiv:2408.07143, 2024 | 1 | 2024 |
| DynamicOED. jl: A Julia package for solving optimum experimental design problems CJ Martensen, C Plate, S Sager Journal of Open Source Software 9 (98), 6605, 2024 | 1 | 2024 |
| models from data useable in pharmacometrics. CJ Martensen, N Korsbo, V Ivaturi, S Sager Training 1490, 705.0, 2032 | | 2032 |
| Two degrees of freedom control of a multistage power-to-methanol reactor T Keßler, C Plate, J Behrens, CJ Martensen, J Leipold, L Kaps, ... Computers & Chemical Engineering 192, 108893, 2025 | | 2025 |
| Data-Driven Discovery of Feedback Mechanisms in Acute Myeloid Leukaemia: Alternatives to classical models using Deep Nonlinear Mixed Effect modeling and Symbolic Regression CJ Martensen, N Korsbo, V Ivaturi, S Sager bioRxiv, 2024.06. 17.599366, 2024 | | 2024 |
| Data driven modeling in Julia CJ Martensen 2022 DigiWell Julia Seminar at the University of Southeastern Norway, 2022 | | 2022 |
| Neural Network Surrogates and Symbolic Regression for System Estimation CJ Martensen, C Rackauckas SIAM Conference on Applications of Dynamical Systems (DS21), 2021 | | 2021 |
Christopher Vincent RackauckasMassachusetts Institute of TechnologyVerified email at mit.edu