TY - JOUR A1 - Kaiser, Eurika A1 - Noack, Bernd R. A1 - Cordier, Laurent A1 - Spohn, Andreas A1 - Segond, Marc A1 - Abel, Markus A1 - Daviller, Guillaume A1 - Osth, Jan A1 - Krajnovic, Sinisa A1 - Niven, Robert K. T1 - Cluster-based reduced-order modelling of a mixing layer JF - Journal of fluid mechanics KW - low-dimensional models KW - nonlinear dynamical systems KW - shear layers Y1 - 2014 U6 - https://doi.org/10.1017/jfm.2014.355 SN - 0022-1120 SN - 1469-7645 VL - 754 SP - 365 EP - 414 PB - Cambridge Univ. Press CY - New York ER - TY - GEN A1 - Kaiser, Eurika A1 - Noack, Bernd R. A1 - Cordier, Laurent A1 - Spohn, Andreas A1 - Segond, Marc A1 - Abel, Markus A1 - Daviller, Guillaume A1 - Osth, Jan A1 - Krajnovic, Sinisa A1 - Niven, Robert K. T1 - Cluster-based reduced-order modelling of a mixing layer T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt, Gunzburger & Lee, Comput. Meth. Appl. Mech. Engng, vol. 196, 2006a, pp. 337-355) and transition matrix models introduced in fluid dynamics in Eckhardt's group (Schneider, Eckhardt & Vollmer, Phys. Rev. E, vol. 75, 2007, art. 066313). CROM constitutes a potential alternative to POD models and generalises the Ulam-Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron-Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Second, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e. g. using finite-time Lyapunov exponent (FTLE) and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative example), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these examples, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 605 KW - low-dimensional models KW - nonlinear dynamical systems KW - shear layers Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-416113 SN - 1866-8372 IS - 605 SP - 365 EP - 414 ER - TY - GEN A1 - Waldrip, Steven H. A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael T1 - Consistent maximum entropy representations of pipe flow networks T2 - AIP conference proceedings N2 - The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study. Y1 - 2017 SN - 978-0-7354-1527-0 U6 - https://doi.org/10.1063/1.4985365 SN - 0094-243X VL - 1853 IS - 1 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael A1 - Waldrip, Steven H. T1 - Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications JF - Entropy N2 - The concept of a "flow network"-a set of nodes and links which carries one or more flows-unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include "observable" constraints on various parameters, "physical" constraints such as conservation laws and frictional properties, and "graphical" constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks. KW - maximum entropy analysis KW - flow network KW - probabilistic inference Y1 - 2019 U6 - https://doi.org/10.3390/e21080776 SN - 1099-4300 VL - 21 IS - 8 SP - 776 PB - MDPI CY - Basel ER - TY - GEN A1 - Waldrip, Steven H. A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, Michael T1 - Maximum entropy analysis of transport networks T2 - AIP conference proceedings N2 - The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made. Y1 - 2017 SN - 978-0-7354-1527-0 U6 - https://doi.org/10.1063/1.4985364 SN - 0094-243X VL - 1853 IS - 1 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Quade, Markus A1 - Abel, Markus A1 - Shafi, Kamran A1 - Niven, Robert K. A1 - Noack, Bernd R. T1 - Prediction of dynamical systems by symbolic regression JF - Physical review : E, Statistical, nonlinear and soft matter physics N2 - We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast. Y1 - 2016 U6 - https://doi.org/10.1103/PhysRevE.94.012214 SN - 2470-0045 SN - 2470-0053 VL - 94 PB - American Society for Pharmacology and Experimental Therapeutics CY - Bethesda ER - TY - JOUR A1 - Waldrip, S. H. A1 - Niven, Robert K. A1 - Abel, Markus A1 - Schlegel, M. T1 - Reduced-Parameter Method for Maximum Entropy Analysis of Hydraulic Pipe Flow Networks JF - Journal of hydraulic engineering N2 - A maximum entropy (MaxEnt) method is developed to predict flow rates or pressure gradients in hydraulic pipe networks without sufficient information to give a closed-form (deterministic) solution. This methodology substantially extends existing deterministic flow network analysis methods. It builds on the MaxEnt framework previously developed by the authors. This study uses a continuous relative entropy defined on a reduced parameter set, here based on the external flow rates. This formulation ensures consistency between different representations of the same network. The relative entropy is maximized subject to observable constraints on the mean values of a subset of flow rates or potential differences, the frictional properties of each pipe, and physical constraints arising from Kirchhoff’s first and second laws. The new method is demonstrated by application to a simple one-loop network and a 1,123-node, 1,140-pipe water distribution network in the suburb of Torrens, Australian Capital Territory, Australia. KW - Maximum entropy method KW - Water distribution systems KW - Hydraulic networks KW - Pipe networks KW - Hydraulic models KW - Nonlinear analysis KW - Probability Y1 - 2017 U6 - https://doi.org/10.1061/(ASCE)HY.1943-7900.0001379 SN - 0733-9429 SN - 1943-7900 VL - 144 IS - 2 PB - American Society of Civil Engineers CY - Reston ER - TY - JOUR A1 - Gout, Julien A1 - Quade, Markus A1 - Shafi, Kamran A1 - Niven, Robert K. A1 - Abel, Markus T1 - Synchronization control of oscillator networks using symbolic regression JF - Nonlinear Dynamics N2 - Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems. KW - Dynamical systems KW - Synchronization control KW - Genetic programming Y1 - 2017 U6 - https://doi.org/10.1007/s11071-017-3925-z SN - 0924-090X SN - 1573-269X VL - 91 IS - 2 SP - 1001 EP - 1021 PB - Springer CY - Dordrecht ER -