Researcher Portfolio
Yildiz, Süleyman
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society
Researcher Profile
Position: Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons291520
Publications
: Yildiz, S., Goyal, P. K., & Benner, P. (in press). Structure-Preserving Learning for Multi-Symplectic PDEs. Advanced Modeling and Simulation in Engineering Sciences. [PubMan] : Goyal, P. K., Yildiz, S., & Benner, P. (2025). Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics. Machine Learning: Science and Technology, 6(1): 015063. doi:10.1088/2632-2153/adb9b5. [PubMan] : Yildiz, S., Goyal, P. K., Bendokat, T., & Benner, P. (2024). Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings. Journal of Machine Learning for Modeling and Computing, 5(2), 45-71. doi:10.1615/JMachLearnModelComput.2024052810. [PubMan]