Low-dimensional Invariant Manifold Research Articles

Reduction to spectral submanifolds in guided car-following with time delay

The reduced order dynamics of a car-following model is investigated when an automated lead vehicle provides guidance to a human driver. Considering the relevant time delay, the system dynamics is governed by a delay differential equation, which has an infinite dimensional spectrum. In order to carry out model reduction, the concept of spectral submanifolds is extended to time delay systems. The dynamics is projected from the infinite dimensional state space to a low-dimensional invariant manifold. The spectral submanifold calculation is carried out both for a real dominant eigenvalue and for a pair of complex conjugate dominant eigenvalues to extract the essential nonlinear dynamics.

Data-driven nonlinear model reduction to spectral submanifolds in mechanical systems.

While data-driven model reduction techniques are well-established for linearizable mechanical systems, general approaches to reducing nonlinearizable systems with multiple coexisting steady states have been unavailable. In this paper, we review such a data-driven nonlinear model reduction methodology based on spectral submanifolds. As input, this approach takes observations of unforced nonlinear oscillations to construct normal forms of the dynamics reduced to very low-dimensional invariant manifolds. These normal forms capture amplitude-dependent properties and are accurate enough to provide predictions for nonlinearizable system response under the additions of external forcing. We illustrate these results on examples from structural vibrations, featuring both synthetic and experimental data.This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.

Strict Nonlinear Normal Modes of Systems Characterized by Scalar Functions on Riemannian Manifolds

For the study of highly nonlinear, conservative dynamic systems, finding special periodic solutions which can be seen as generalization of the well-known normal modes of linear systems is very attractive. However, the study of low-dimensional invariant manifolds in the form of nonlinear normal modes is rather a niche topic, treated mainly in the context of structural mechanics for systems with Euclidean metrics, i.e., for point masses connected by nonlinear springs. In our previous research [1], [16], [17] we recognized, however, that a very rich structure of periodic and low-dimensional solutions exist also within nonlinear systems such as elastic multi-body systems encountered in the biomechanics of humans and animals or of humanoid and quadruped robots, which are characterized by a non-constant metric tensor. This letter briefly discusses different generalizations of linear oscillation modes to nonlinear systems and proposes a definition of strict nonlinear normal modes, which matches most of the relevant properties of the linear modes. The main contributions are a theorem providing necessary and sufficient conditions for the existence of strict oscillation modes on systems endowed with a Riemannian metric and a potential field as well as a constructive example of designing such modes in the case of an elastic double pendulum.

An Invariant-Manifold Approach to Lumping

Differential equation models of chemical or biochemical systems usually display multiple, widely varying time scales, i.e. they are stiff. After the decay of transients, trajectories of these systems approach low-dimensional invariant manifolds on which the eventual attractor (an equilibrium point in a closed system) is approached, and in which this attractor is embedded. Computing one of these slow invariant manifolds (SIMs) results in a reduced model of dimension equal to the dimension of the SIM. Another approach to model reduction involves lumping, the formulation of a reduced set of variables that combine the original model variables and in terms of which the reduced model is framed. In this study, we combine lumping with a constructive method for SIMs based on the iterative solution of the invariance equation. We illustrate these methods using a simple model of a linear metabolic pathway, and a model for hydrogen oxidation. The former is treated with a linear lumping function, while a nonlinear lumping function based on a Lyapunov function is used in the latter.

Approximation of slow and fast dynamics in multiscale dynamical systems by the linearized Relaxation Redistribution Method

In this paper, we introduce a fictitious dynamics for describing the only fast relaxation of a stiff ordinary differential equation (ODE) system towards a stable low-dimensional invariant manifold in the phase-space ( slow invariant manifold – SIM). As a result, the demanding problem of constructing SIM of any dimensions is recast into the remarkably simpler task of solving a properly devised ODE system by stiff numerical schemes available in the literature. In the same spirit, a set of equations is elaborated for local construction of the fast subspace, and possible initialization procedures for the above equations are discussed. The implementation to a detailed mechanism for combustion of hydrogen and air has been carried out, while a model with the exact Chapman–Enskog solution of the invariance equation is utilized as a benchmark.

Invariant Manifolds of Binomial-Like Nonautonomous Master Equations

It has recently been shown that certain finite-state jump processes used to model ion channel kinetics give rise to nonautonomous master equations which possess low-dimensional invariant manifolds. In particular, for the model of an ion channel with many identical and independent subunits existing in either an open or closed state, the one-dimensional manifold of binomial distributions, whose parameter is the probability of a single subunit being in an open state, is an invariant manifold of the associated master equation, and it is the only such one-dimensional manifold. In this paper, we make explicit how the invariance of this manifold arises from the structure of the transition rate matrix of the master equation. We also show that, despite not having any apparent underlying structure or symmetry akin to the ion channel example, there exists a two-parameter family of master equations that shares the essential features of this example and therefore possesses unique, one-dimensional invariant manifolds similar to the manifold of binomial distributions. The mere existence of such a family suggests that there may be other nonautonomous master equations that are devoid of any obvious structure but nevertheless possess invariant manifolds of low dimension.

Invariant submanifold for series arrays of Josephson junctions

We study the nonlinear dynamics of series arrays of Josephson junctions in the large-N limit, where N is the number of junctions in the array. The junctions are assumed to be identical, overdamped, driven by a constant bias current, and globally coupled through a common load. Previous simulations of such arrays revealed that their dynamics are remarkably simple, hinting at the presence of some hidden symmetry or other structure. These observations were later explained by the discovery of N−3 constants of motion, the choice of which confines the resulting flow in phase space to a low-dimensional invariant manifold. Here we show that the dimensionality can be reduced further by restricting attention to a special family of states recently identified by Ott and Antonsen. In geometric terms, the Ott–Antonsen ansatz corresponds to an invariant submanifold of dimension one less than that found earlier. We derive and analyze the flow on this submanifold for two special cases: an array with purely resistive loading and another with resistive-inductive-capacitive loading. Our results recover (and in some instances improve) earlier findings based on linearization arguments.

Unstable recurrent patterns in Kuramoto-Sivashinsky dynamics

We undertake an exploration of recurrent patterns in the antisymmetric subspace of the one-dimensional Kuramoto-Sivashinsky system. For a small but already rather "turbulent" system, the long-time dynamics takes place on a low-dimensional invariant manifold. A set of equilibria offers a coarse geometrical partition of this manifold. The Newton descent method enables us to determine numerically a large number of unstable spatiotemporally periodic solutions. The attracting set appears surprisingly thin-its backbone consists of several Smale horseshoe repellers, well approximated by intrinsic local one-dimensional return maps, each with an approximate symbolic dynamics. The dynamics appears decomposable into chaotic dynamics within such local repellers, interspersed by rapid jumps between them.

Transversal dynamics of a non-locally-coupled map lattice

A lattice of coupled chaotic dynamical systems may exhibit a completely synchronized state, which defines a low-dimensional invariant manifold in phase space. However, the high dimensionality of the latter typically yields a complex dynamics with many features like chaos suppression, quasiperiodicity, multistability, and intermittency. Such phenomena are described by considering the transversal dynamics to the synchronization manifold for a coupled logistic map lattice with a long-range coupling prescription.

Mechanism of Psychoactive Drug Action in the Brain: Simulation Modeling of GABAA Receptor Interactions at Non-Equilibrium Conditions

Synaptic transmission requires that the binding of the transmitter to the receptor to occur under rapidly changing transmitter levels, and this binding interaction is unlikely to be at equilibrium. We have sought to numerically solve for binding kinetics using ordinary differential equations and simultaneous difference equations for use in stochastic conditions. The reaction scheme of GABA interacting with the ligand-gated ion-channel demonstrates numerical stiffness. Implicit methods (Backward Euler, ode23s) performed orders of magnitude better than explicit methods (Forward Euler, ode23, RK4, ode45) in terms of step size required for stability, number of steps and cpu time. Interestingly, upon solving the system of 8 ordinary differential equations for the GABA reaction scheme we observed the existence of low dimensional invariant manifolds that may have important consequences for information processing in synapses. We also describe a mathematical approach that models complex receptor interactions in which the timing and amplitude of transmitter release are noisy. Exact solutions for simple bimolecular interactions that include stoichiometric interactions and receptor transitions can be used to model complex reaction schemes. We used the difference method to investigate the information processing capabilities of GABA(A) receptors and to predict how pharmacological agents may modify these properties. Initial simulations using a model for heterosynaptic regulation shows that signal to noise ratios can be decreased in the presence of background presynaptic activity both in the presence and absence of chlorpromazine. These types of simulations provide a platform for investigating the effect of psycho-active drugs on complex responses of transmitter-receptor interactions in noisy cellular environments such as the synapse. Understanding this process of transmitter-receptor interactions may be useful in the development of more specific and highly targeted modes of action.

The functional equation truncation method for approximating slow invariant manifolds: A rapid method for computing intrinsic low-dimensional manifolds

A slow manifold is a low-dimensional invariant manifold to which trajectories nearby are rapidly attracted on the way to the equilibrium point. The exact computation of the slow manifold simplifies the model without sacrificing accuracy on the slow time scales of the system. The Maas-Pope intrinsic low-dimensional manifold (ILDM) [Combust. Flame 88, 239 (1992)] is frequently used as an approximation to the slow manifold. This approximation is based on a linearized analysis of the differential equations and thus neglects curvature. We present here an efficient way to calculate an approximation equivalent to the ILDM. Our method, called functional equation truncation (FET), first develops a hierarchy of functional equations involving higher derivatives which can then be truncated at second-derivative terms to explicitly neglect the curvature. We prove that the ILDM and FET-approximated (FETA) manifolds are identical for the one-dimensional slow manifold of any planar system. In higher-dimensional spaces, the ILDM and FETA manifolds agree to numerical accuracy almost everywhere. Solution of the FET equations is, however, expected to generally be faster than the ILDM method.

Invariant manifolds describing the dynamics of a hyperbolic–parabolic equation from nonlinear viscoelasticity

The governing equations for a collection of dynamical problems for heavy rigid attachments carried by light, deformable, nonlinearly viscoelastic bodies are studied. These equations are a discretization of a nonlinear hyperbolic–parabolic partial differential equation coupled to a dynamical boundary condition. A small parameter measuring the ratio of the mass of the deformable body to the mass of the rigid attachment is introduced, and geometric singular perturbation theory is applied to reduce the dynamics to the dynamics of the slow system. Fenichel theory is then applied to the regular perturbation of the slow system to prove the existence of a low-dimensional invariant manifold within the dynamics of the high-dimensional discretization.

The invariant constrained equilibrium edge preimage curve method for the dimension reduction of chemical kinetics

This work addresses the construction and use of low-dimensional invariant manifolds to simplify complex chemical kinetics. Typically, chemical kinetic systems have a wide range of time scales. As a consequence, reaction trajectories rapidly approach a hierarchy of attracting manifolds of decreasing dimension in the full composition space. In previous research, several different methods have been proposed to identify these low-dimensional attracting manifolds. Here we propose a new method based on an invariant constrained equilibrium edge (ICE) manifold. This manifold (of dimension nr) is generated by the reaction trajectories emanating from its (nr-1)-dimensional edge, on which the composition is in a constrained equilibrium state. A reasonable choice of the nr represented variables (e.g., nr "major" species) ensures that there exists a unique point on the ICE manifold corresponding to each realizable value of the represented variables. The process of identifying this point is referred to as species reconstruction. A second contribution of this work is a local method of species reconstruction, called ICE-PIC, which is based on the ICE manifold and uses preimage curves (PICs). The ICE-PIC method is local in the sense that species reconstruction can be performed without generating the whole of the manifold (or a significant portion thereof). The ICE-PIC method is the first approach that locally determines points on a low-dimensional invariant manifold, and its application to high-dimensional chemical systems is straightforward. The "inputs" to the method are the detailed kinetic mechanism and the chosen reduced representation (e.g., some major species). The ICE-PIC method is illustrated and demonstrated using an idealized H2O system with six chemical species. It is then tested and compared to three other dimension-reduction methods for the test case of a one-dimensional premixed laminar flame of stoichiometric hydrogen/air, which is described by a detailed mechanism containing nine species and 21 reactions. It is shown that the error incurred by the ICE-PIC method with four represented species is small across the whole flame, even in the low temperature region.