WiSe 18/19: Seminar: Modellreduktion
Peter Koltai, Nikki Vercauteren
Comments
Studying natural processes for real life applications often involves the modeling and computational simulations of complex processes occurring on many time or spatial scales. The governing equations that represent the dynamics are therefore typically high-dimensional and nonlinear. On the one hand, that makes using tools from dynamical systems theory difficult. On the other hand, each numerical simulation will inevitably fail to resolve so-called unresolved scales or subgrid-scales.
Model reduction aims at simplifying the governing equations by finding low-dimensional models that approximate the full high-dimensional dynamics.
This seminar will discuss model reduction strategies often used in fluid dynamics, climate sciences, molecular dynamics, and control theory.
Methods and conceptual frameworks to be discussed include Proper Orthogonal Decomposition (POD), balanced truncation in control, the Mori-Zwanzig formalism for stochastic parameterization of fast degrees of freedom, and data-driven model reduction techniques such as Markov State Models (MSM) and Hidden Markov Models (HMM).
closeSuggested reading
Antoulas AC 2005. Approximation of Large-Scale Dynamical Systems, SIAM Publications.
Bittracher A, Koltai P, Klus S et al 2018. Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics. Journal of Nonlinear Science 28:471. https://doi.org/10.1007/s00332-017-9415-0
Chorin AJ, Hald OH, Kupferman R 2000. Optimal prediction and the Mori–Zwanzig representation of irreversible processes. Proceedings of the National Academy of Sciences, 97(7):2968–2973.
Gottwald GA, Crommelin DT, Franzke CLE 2017. Stochastic climate theory. In: Nonlinear and stochastic climate dynamics. Cambridge Univ. Press, Cambridge, 209–240.
Metzner P, Putzig L, Horenko I 2012. Analysis of Persistent Nonstationary Time Series and Applications. Commun. Appl. Math. Comput. Sci. 7:175–229.
Rowley CW 2005. Model reduction for fluids, using balanced proper orthogonal decomposition. International Journal of Bifurcation and Chaos 15:997–1013.
Schütte C, Sarich M 2013. Metastability and Markov State Models in Molecular Dynamics: Modeling, Analysis, Algorithmic Approaches. Courant Lecture Notes.
close16 Class schedule
Regular appointments