Control - Math bibliographies - in Harvard style

In-text: (Akemann, Burda and Kieburg, 2019)

Your Bibliography: Akemann, G., Burda, Z. and Kieburg, M., 2019. From integrable to chaotic systems: Universal local statistics of Lyapunov exponents. EPL (Europhysics Letters), 126(4), p.40001.

In-text: (Ayers et al., 2022)

Your Bibliography: Ayers, D., Lau, J., Amezcua, J., Carrassi, A. and Ojha, V., 2022. Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents. [online] arXiv.org. Available at: <https://arxiv.org/abs/2202.04944> [Accessed 9 July 2022].

In-text: (Churchill and Xiu, 2022)

Your Bibliography: Churchill, V. and Xiu, D., 2022. Deep Learning of Chaotic Systems from Partially-Observed Data. [online] arXiv.org. Available at: <https://arxiv.org/abs/2205.08384> [Accessed 9 July 2022].

In-text: (Manzano et al., 2022)

Your Bibliography: Manzano, S., Sundaram, V., Xu, A., Ly, K., Rentschler, M., Shepherd, R. and Correll, N., 2022. Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems. [online] arXiv.org. Available at: <https://arxiv.org/abs/2205.10940> [Accessed 9 July 2022].

In-text: (Zhou, Quartz, De Sterck and Liu, 2022)

Your Bibliography: Zhou, R., Quartz, T., De Sterck, H. and Liu, J., 2022. Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees. [online] arXiv.org. Available at: <https://arxiv.org/abs/2206.01913> [Accessed 9 July 2022].