Publications DMP group

Nateghi, V.; Manzi, M.: Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field. Advances in Space Research 72 (10), pp. 4106 - 4114 (2023)

Nüske, F.; Klus, S.: Efficient Approximation of Molecular Kinetics using Random Fourier Features. The Journal of Chemical Physics 159 (7), 074105 (2023)

Nüske, F.; Peitz, S.; Philipp, F.; Schaller, M.; Worthmann, K.: Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science 33, 14 (2023)

Yang, W.; Templeton, C.; Rosenberger, D.; Bittracher, A.; Nüske, F.; Noé, F.; Clementi, C.: Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics. ACS Central Science 9 (2), pp. 186 - 196 (2023)

Schaller, M.; Worthmann, K.; Philipp, F.; Peitz, S.; Nüske, F.: Towards reliable data-based optimal and predictive control using extended DMD. 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022, Canberra, Australia, January 04, 2023 - January 06, 2023. IFAC-PapersOnLine 56 (1), pp. 169 - 174 (2023)

Philipp, F.; Schaller, M.; Worthmann, K.; Peitz, S.; Nüske, F.: Error analysis of kernel EDMD for prediction and control in the Koopman framework. (2023)

Peitz, S.; Harder, H.; Nüske, F.; Philipp, F.; Schaller, M.; Worthmann, K.: Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems. (2023)

Philipp, F.; Schaller, M.; Worthmann, K.; Peitz, S.; Nüske, F.: Error bounds for kernel-based approximations of the Koopman operator. (2023)