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Equilibrium Models for System Identification
Deep equilibrium networks and their relation to system theory, part of the seminar Machine Learning in the Sciences by Mathias Niepert. The code for the examples shown is available on GitHub
Carbonara
Some dolce vita at home.
portfolio
publications
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees
Technical report, arXiv, 2022
We represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances and introduce parameter constraints that guarantee finite gain stability, applied to learning ship motion in open water.
Recommended citation: Frank, D., Aspandi Latif, D., Muehlebach, M., Unger, B., & Staab, S. (2022). "Robust recurrent neural network to identify ship motion in open water with performance guarantees." Technical report, arXiv:2212.05781. https://arxiv.org/abs/2212.05781
Learning the Dynamics of Nonlinear Systems with Regional Stability Guarantees through Linear Matrix Inequality Constraints
Accepted at the IFAC World Congress 2026, Busan, South Korea (preprint on arXiv), 2026
We derive linear matrix inequality constraints on recurrent neural network parameters that certify regional stability guarantees for learned nonlinear system dynamics.
Recommended citation: Frank, D., Shakib, F., & Staab, S. (2026). "Learning the dynamics of nonlinear systems with regional stability guarantees through linear matrix inequality constraints." arXiv:2605.18292. Accepted at the IFAC World Congress 2026. https://arxiv.org/abs/2605.18292
Regularised Neural Network-Based Nonlinear System Identification with Prior System Knowledge
International Journal of Control, 2026
We propose a regularisation approach for neural network-based nonlinear system identification that incorporates prior system knowledge.
Recommended citation: Frank, D., Holicki, T., Scherer, C. W., & Staab, S. (2026). "Regularised neural network-based nonlinear system identification with prior system knowledge." International Journal of Control. https://doi.org/10.1080/00207179.2026.2679228
talks
Constrained Residual RNN for Nonlinear System Identification
Presented joint work with Tobias Holicki, Steffen Staab, and Carsten W. Scherer at the Workshop on Nonlinear System Identification Benchmarks.
Generalized Sector Conditions for Neural Network Architectures in System Identification
Presented joint work with Tobias Holicki and Steffen Staab at the Workshop on Nonlinear System Identification Benchmarks.
Learning the Dynamics of Nonlinear Systems with a Certified Output Set through Linear Matrix Inequalities
Presented joint work with Fahim Shakib and Steffen Staab at the Workshop on Nonlinear System Identification Benchmarks.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.