References
For scientific machine learning background, see [1]. The bibliography also includes related work on dynamic experimental design.
- [1]
- C. Rackauckas, Y. Ma, C. J. Martensen, C. Warner, K. Zubov, R. Supekar, D. Skinner, A. Ramadhan and A. Edelman. Universal Differential Equations for Scientific Machine Learning (Nov 2021), arXiv:2001.04385 [cs]. Accessed on Dec 3, 2025. ↩1
- [2]
- C. J. Martensen, C. Plate and S. Sager. DynamicOED.Jl: A Julia Package for Solving Optimumexperimental Design Problems. JOSS 9, 6605 (2024). Accessed on Dec 2, 2025.
- [3]
- C. Plate, C. J. Martensen and S. Sager. Optimal Experimental Design for Universal Differential Equations. IEEE Trans. Automat. Contr., 1–16 (2025). Accessed on Dec 2, 2025.
- [4]
- S. Sager. Sampling Decisions in Optimum Experimental Design in the Light of Pontryagin's Maximum Principle. SIAM J. Control Optim. 51, 3181–3207 (2013). Accessed on Nov 6, 2023.