## Publications

83 results found

Gvalani R, Carrillo de la Plata JA, Pavliotis G,
et al., Long-time behaviour and phase transitions for the McKean—Vlasov equation on the torus, *Archive for Rational Mechanics and Analysis*, ISSN: 0003-9527

Abdulle A, Pavliotis GA, Vilmart G, 2019, Accelerated convergence to equilibrium and reduced asymptotic variance for Langevin dynamics using Stratonovich perturbations, *Comptes Rendus Mathematique*, Vol: 357, Pages: 349-354, ISSN: 1631-073X

© 2019 In this paper, we propose a new approach for sampling from probability measures in, possibly, high-dimensional spaces. By perturbing the standard overdamped Langevin dynamics by a suitable Stratonovich perturbation that preserves the invariant measure of the original system, we show that accelerated convergence to equilibrium and reduced asymptotic variance can be achieved, leading, thus, to a computationally advantageous sampling algorithm. The new perturbed Langevin dynamics is reversible with respect to the target probability measure and, consequently, does not suffer from the drawbacks of the nonreversible Langevin samplers that were introduced in C.-R. Hwang et al. (1993)[1]and studied in, e.g., T. Lelièvre et al. (2013)[2]and A.B. Duncan et al. (2016)[3], while retaining all of their advantages in terms of accelerated convergence and reduced asymptotic variance. In particular, the reversibility of the dynamics ensures that there is no oscillatory transient behaviour. The improved performance of the proposed methodology, in comparison to the standard overdamped Langevin dynamics and its nonreversible perturbation, is illustrated on an example of sampling from a two-dimensional warped Gaussian target distribution.

Nüsken N, Pavliotis GA, 2019, Constructing sampling schemes via coupling: Markov semigroups and optimal transport, *SIAM/ASA Journal on Uncertainty Quantification*, Vol: 7, Pages: 324-382, ISSN: 2166-2525

In this paper we develop a general framework for constructing and analyzing coupled Markov chain Monte Carlo samplers, allowing for both (possibly degenerate) diffusion and piecewise deterministic Markov processes. For many performance criteria of interest, including the asymptotic variance, the task of finding efficient couplings can be phrased in terms of problems related to optimal transport theory. We investigate general structural properties, proving a singularity theorem that has both geometric and probabilistic interpretations. Moreover, we show that those problems can often be solved approximately and support our findings with numerical experiments. For the particular objective of estimating the variance of a Bayesian posterior, our analysis suggests using novel techniques in the spirit of antithetic variates. Addressing the convergence to equilibrium of coupled processes we furthermore derive a modified Poincaré inequality.

Gomes SN, Kalliadasis S, Pavliotis GA,
et al., 2019, Dynamics of the Desai-Zwanzig model in multiwell and random energy landscapes, *Physical Review E*, Vol: 99, ISSN: 2470-0045

We analyze a variant of the Desai-Zwanzig model [J. Stat. Phys. 19, 1 (1978)]. In particular, we study stationary states of the mean field limit for a system of weakly interacting diffusions moving in a multiwell potential energy landscape, coupled via a Curie-Weiss type (quadratic) interaction potential. The location and depth of the local minima of the potential are either deterministic or random. We characterize the structure and nature of bifurcations and phase transitions for this system, by means of extensive numerical simulations and of analytical calculations for an explicitly solvable model. Our numerical experiments are based on Monte Carlo simulations, the numerical solution of the time-dependent nonlinear Fokker-Planck (McKean-Vlasov) equation, the minimization of the free-energy functional, and a continuation algorithm for the stationary solutions.

Durán-Olivencia MA, Gvalani RS, Kalliadasis S,
et al., 2019, Instability, rupture and fluctuations in thin liquid films: Theory and computations, *Journal of Statistical Physics*, Vol: 174, Pages: 579-604, ISSN: 0022-4715

Thin liquid films are ubiquitous in natural phenomena and technological applications. They have been extensively studied via deterministic hydrodynamic equations, but thermal fluctuations often play a crucial role that needs to be understood. An example of this is dewetting, which involves the rupture of a thin liquid film and the formation of droplets. Such a process is thermally activated and requires fluctuations to be taken into account self-consistently. In this work we present an analytical and numerical study of a stochastic thin-film equation derived from first principles. Following a brief review of the derivation, we scrutinise the behaviour of the equation in the limit of perfectly correlated noise along the wall-normal direction, as opposed to the perfectly uncorrelated limit studied by Grün et al. (J Stat Phys 122(6):1261–1291, 2006). We also present a numerical scheme based on a spectral collocation method, which is then utilised to simulate the stochastic thin-film equation. This scheme seems to be very convenient for numerical studies of the stochastic thin-film equation, since it makes it easier to select the frequency modes of the noise (following the spirit of the long-wave approximation). With our numerical scheme we explore the fluctuating dynamics of the thin film and the behaviour of its free energy in the vicinity of rupture. Finally, we study the effect of the noise intensity on the rupture time, using a large number of sample paths as compared to previous studies.

Tomlin R, Gomes SN, Pavliotis G,
et al., Optimal control of thin liquid films and transverse mode effects, *SIAM Journal on Applied Dynamical Systems*, ISSN: 1536-0040

We consider the control of a three-dimensional thin liquid film on a flat substrate, inclined at anon-zero angle to the horizontal. Controls are applied via same-fluid blowing and suction throughthe substrate surface. We consider both overlying and hanging films, where the liquid lies above orbelow the substrate, respectively. We study the weakly nonlinear evolution of the system, which isgoverned by a forced Kuramoto–Sivashinsky equation in two space dimensions. The uncontrolledproblem exhibits three ranges of dynamics depending on the incline of the substrate: stable flat filmsolution, bounded chaotic dynamics, or unbounded exponential growth of unstable transverse modes.We proceed with the assumption that we may actuate at every point on the substrate. The mainfocus is the optimal control problem, which we first study in the special case that the forcing mayonly vary in the spanwise direction. The structure of the Kuramoto–Sivashinsky equation allows theexplicit construction of optimal controls in this case using the classical theory of linear quadraticregulators. Such controls are employed to prevent the exponential growth of transverse waves inthe case of a hanging film, revealing complex dynamics for the streamwise and mixed modes. Wethen consider the optimal control problem in full generality, and prove the existence of an optimalcontrol. For numerical simulations, we employ an iterative gradient descent algorithm. In the finalsection, we consider the effects of transverse mode forcing on the chaotic dynamics present in thestreamwise and mixed modes for the case of a vertical film flow. Coupling through nonlinearityallows us to reduce the average energy in solutions without directly forcing the linearly unstable dominant modes.

Schmuck M, Pavliotis GA, Kalliadasis S, 2019, Recent advances in the evolution of interfaces: Thermodynamics, upscaling, and universality, *Computational Materials Science*, Vol: 156, Pages: 441-451, ISSN: 0927-0256

We consider the evolution of interfaces in binary mixtures permeating strongly heterogeneous systems such as porous media. To this end, we first review available thermodynamic formulations for binary mixtures based on general reversible-irreversible couplings and the associated mathematical attempts to formulate a non-equilibrium variational principle in which these non-equilibrium couplings can be identified as minimizers. Based on this, we investigate two microscopic binary mixture formulations fully resolving heterogeneous/perforated domains: (a) a flux-driven immiscible fluid formulation without fluid flow; (b) a momentum-driven formulation for quasi-static and incompressible velocity fields. In both cases we state two novel, reliably upscaled equations for binary mixtures/multiphase fluids in strongly heterogeneous systems by systematically taking thermodynamic features such as free energies into account as well as the system's heterogeneity defined on the microscale such as geometry and materials (e.g. wetting properties). In the context of (a), we unravel a universality with respect to the coarsening rate due to its independence of the system's heterogeneity, i.e. the well-known O(t1/3)-behaviour for homogeneous systems holds also for perforated domains. Finally, the versatility of phase field equations and their thermodynamic foundation relying on free energies, make the collected recent developments here highly promising for scientific, engineering and industrial applications for which we provide an example for lithium batteries.

Duncan A, Zygalakis K, Pavliotis G, 2018, Nonreversible Langevin Samplers: Splitting Schemes, Analysis and Implementation

For a given target density, there exist an infinite number of diffusion processes which are ergodic with respect to this density. As observed in a number of papers, samplers based on nonreversible diffusion processes can significantly outperform their reversible counterparts both in terms of asymptotic variance and rate of convergence to equilibrium. In this paper, we take advantage of this in order to construct efficient sampling algorithms based on the Lie-Trotter decomposition of a nonreversible diffusion process into reversible and nonreversible components. We show that samplers based on this scheme can significantly outperform standard MCMC methods, at the cost of introducing some controlled bias. In particular, we prove that numerical integrators constructed according to this decomposition are geometrically ergodic and characterise fully their asymptotic bias and variance, showing that the sampler inherits the good mixing properties of the underlying nonreversible diffusion. This is illustrated further with a number of numerical examples ranging from highly correlated low dimensional distributions, to logistic regression problems in high dimensions as well as inference for spatial models with many latent variables.

Craster R, Guenneau S, Hutridurga Ramaiah H,
et al., 2018, Cloaking via mapping for the heat equation, *Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal*, Vol: 16, Pages: 1146-1174, ISSN: 1540-3459

This paper explores the concept of near-cloaking in the context of time-dependentheat propagation. We show that after the lapse of a certain threshold time, the boundary measure-ments for the homogeneous heat equation are close to the cloaked heat problem in a certain Sobolevspace norm irrespective of the density-conductivity pair in the cloaked region. A regularised trans-formation media theory is employed to arrive at our results. Our proof relies on the study of the longtime behaviour of solutions to the parabolic problems with high contrast in density and conductivitycoefficients. It further relies on the study of boundary measurement estimates in the presence of smalldefects in the context of steady conduction problem. We then present some numerical examples to illustrate our theoretical results.

Noronha Moreira Antunes Gomes ST, Pavliotis GA, 2018, Mean field limits for interacting diffusions in a two-scale potential, *Journal of Nonlinear Science*, Vol: 28, Pages: 905-941, ISSN: 0938-8974

In this paper we study the combined mean field and homogenization limits for a system of weakly interacting diffusions moving in a two-scale, locally periodic confining potential, of the form considered in [13]. We show that, although the mean field and homogenization limits commute for finite times, they do not, in general, commute in the long time limit. In particular, the bifurcation diagrams for the stationary states can be different depending on the order with which we take the two limits. Furthermore, we construct the bifurcation diagram for the stationary McKean-Vlasov equation in a two-scale potential, before passing to the homogenization limit, and we analyze the effect of the multiple local minima in the confining potential on the number and the stability of stationary solutions.

Ellam L, Girolami M, Pavliotis GA,
et al., 2018, Stochastic modelling of urban structure, *Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences*, ISSN: 1364-5021

The building of mathematical and computer models of cities has a long history. The core elements are models of flows (spatial interaction) and the dynamics of structural evolution. In this article, we develop a stochastic model of urban structure to formally account for uncertainty arising from less predictable events. Standard practice has been to calibrate the spatial interaction models independently and to explore the dynamics through simulation. We present two significant results that will be transformative for both elements. First, we represent the structural variables through a single potential function and develop stochastic differential equations to model the evolution. Second, we show that the parameters of the spatial interaction model can be estimated from the structure alone, independently of flow data, using the Bayesian inferential framework. The posterior distribution is doubly intractable and poses significant computational challenges that we overcome using Markov chain Monte Carlo methods. We demonstrate our methodology with a case study on the London, UK, retail system.

Duong MH, Pavliotis GA, 2018, Mean field limits for non-Markovian interacting particles: Convergence to equilibrium, generic formalism, asymptotic limits and phase transitions, *Communications in Mathematical Sciences*, Vol: 16, Pages: 2199-2230, ISSN: 1539-6746

© 2018 International Press. In this paper, we study the mean field limit of weakly interacting particles with memory that are governed by a system of non-Markovian Langevin equations. Under the assumption of quasi- Markovianity (i.e. the memory in the system can be described using a finite number of auxiliary processes), we pass to the mean field limit to obtain the corresponding McKean-Vlasov equation in an extended phase space. For the case of a quadratic confining potential and a quadratic (Curie- Weiss) interaction, we obtain the fundamental solution (Green's function). For nonconvex confining potentials, we characterize the stationary state(s) of the McKean-Vlasov equation, and we show that the bifurcation diagram of the stationary problem is independent of the memory in the system. In addition, we show that the McKean-Vlasov equation for the non-Markovian dynamics can be written in the GENERIC formalism and we study convergence to equilibrium and the Markovian asymptotic limit.

Duncan AB, Nusken N, Pavliotis GA, 2017, Using perturbed underdamped langevin dynamics to efficiently sample from probability distributions, *Journal of Statistical Physics*, Vol: 169, Pages: 1098-1131, ISSN: 1572-9613

In this paper we introduce and analyse Langevin samplers that consist of perturbations of the standard underdamped Langevin dynamics. The perturbed dynamics is such that its invariant measure is the same as that of the unperturbed dynamics. We show that appropriate choices of the perturbations can lead to samplers that have improved properties, at least in terms of reducing the asymptotic variance. We present a detailed analysis of the new Langevin sampler for Gaussian target distributions. Our theoretical results are supported by numerical experiments with non-Gaussian target measures.

Craster R, Guenneau S, Hutridurga H, et al., 2017, Regularized transformation optics for transient heat transfer, 2017 11th International Congress on Engineered Material Platforms for Novel Wave Phenomena (METAMATERIALS), Publisher: IEEE, Pages: 127-129

Abdulle A, Pavliotis GA, Vaes U, 2017, Spectral Methods for Multiscale Stochastic Differential Equations, *SIAM/ASA Journal on Uncertainty Quantification*, Vol: 5, Pages: 720-761, ISSN: 2166-2525

This paper presents a new method for the solution of multiscale stochastic differential equations atthe diffusive time scale. In contrast to averaging-based methods, e.g., the heterogeneous multiscalemethod (HMM) or the equation-free method, which rely on Monte Carlo simulations, in this paperwe introduce a new numerical methodology that is based on a spectral method. In particular, we usean expansion in Hermite functions to approximate the solution of an appropriate Poisson equation,which is used in order to calculate the coefficients of the homogenized equation. Spectral convergenceis proved under suitable assumptions. Numerical experiments corroborate the theory and illustratethe performance of the method. A comparison with the HMM and an application to singularlyperturbed stochastic PDEs are also presented.

Tomlin RJ, Papageorgiou DT, Pavliotis GA, Three-dimensional wave evolution on electrified falling films, *Journal of Fluid Mechanics*, Vol: 822, Pages: 54-79, ISSN: 1469-7645

We consider the full three-dimensional dynamics of a thin falling liquid film on a flat plate inclined at some non-zero angle to the horizontal. In addition to gravitational effects, the flow is driven by an electric field which is normal to the substrate far from the flow. This extends the work of Tseluiko & Papageorgiou (J. Fluid Mech., vol. 556, 2006b, pp. 361–386) by including transverse dynamics. We study both the cases of overlying and hanging films, where the liquid lies above or below the substrate, respectively. Starting with the Navier–Stokes equations coupled with electrostatics, a fully nonlinear two-dimensional Benney equation for the interfacial dynamics is derived, valid for waves that are long compared to the film thickness. The weakly nonlinear evolution is governed by a Kuramoto–Sivashinsky equation with a non-local term due to the electric field effect. The electric field term is linearly destabilising and produces growth rates proportional to $|\unicode[STIX]{x1D743}|^{3}$ , where $\unicode[STIX]{x1D743}$ is the wavenumber vector of the perturbations. It is found that transverse gravitational instabilities are always present for hanging films, and this leads to unboundedness of nonlinear solutions even in the absence of electric fields – this is due to the anisotropy of the nonlinearity. For overlying films and a restriction on the strength of the electric field, the equation is well-posed in the sense that it possesses bounded solutions. This two-dimensional equation is studied numerically for the case of periodic boundary conditions in order to assess the effects of inertia, electric field strength and the size of the periodic domain. Rich dynamical behaviours are observed and reported. For subcritical Reynolds number flows, a sufficiently strong electric field can promote non-trivial dynamics for some choices of domain size, leading to fully two-dimensional evolutions of the interface. We also observe two-dimensiona

Noronha Moreira Antunes Gomes ST, Kalliadasis S, Papageorgiou DT,
et al., 2017, Controlling roughening processes in the stochastic Kuramoto-Sivashinsky equation, *Physica D - Nonlinear Phenomena*, Vol: 348, Pages: 33-43, ISSN: 0167-2789

We present a novel control methodology to control the roughening processes of semilinear parabolic stochastic partial differential equations in one dimension, which we exemplify with the stochastic Kuramoto-Sivashinsky equation. The original equation is split into a linear stochastic and a nonlinear deterministic equation so that we can apply linear feedback control methods. Our control strategy is then based on two steps: first, stabilize the zero solution of the deterministic part and, second, control the roughness of the stochastic linear equation. We consider both periodic controls and point actuated ones, observing in all cases that the second moment of the solution evolves in time according to a power-law until it saturates at the desired controlled value.

Duncan AB, Kalliadasis S, Pavliotis GA,
et al., 2016, Noise-induced transitions in rugged energy landscapes, *Physical Review E*, Vol: 94, ISSN: 1539-3755

We consider the problem of an overdamped Brownian particle moving in multiscale potential with N+1 characteristic length scales: the macroscale and N separated microscales. We show that the coarse-grained dynamics is given by an overdamped Langevin equation with respect to the free energy and with a space-dependent diffusion tensor, the calculation of which requires the solution of N fully coupled Poisson equations. We study in detail the structure of the bifurcation diagram for one-dimensional problems, and we show that the multiscale structure in the potential leads to hysteresis effects and to noise-induced transitions. Furthermore, we obtain an explicit formula for the effective diffusion coefficient for a self-similar separable potential, and we investigate the limit of infinitely many small scales.

Duncan AB, Pavliotis GA, Lelievre T, 2016, Variance Reduction using Nonreversible Langevin Samplers, *Journal of Statistical Physics*, Vol: 163, Pages: 457-491, ISSN: 1572-9613

A standard approach to computing expectations with respect to a given target measure is to introduce an overdamped Langevin equation which is reversible with respect to the target distribution, and to approximate the expectation by a time-averaging estimator. As has been noted in recent papers, introducing an appropriately chosen nonreversiblecomponent to the dynamics is beneficial, both in terms of reducing the asymptotic variance and of speeding up convergence to the target distribution. In this paper we present a detailed study of the dependence of the asymptotic variance on the deviation from reversibility. Our theoretical findings are supported by numerical simulations.

Gomes SN, Papageorgiou DT, Pavliotis GA, 2016, Stabilizing non-trivial solutions of the generalized Kuramoto-Sivashinsky equation using feedback and optimal control, *IMA JOURNAL OF APPLIED MATHEMATICS*, Vol: 82, Pages: 158-194, ISSN: 0272-4960

Bonaille-Noel V, Carrillo de la Plata J, Goudon T,
et al., 2016, Efficient numerical calculation of drift and diffusion coefficients in the diffusion approximation of kinetic equations, *IMA Journal of Numerical Analysis*, Vol: 36, Pages: 1536-1569, ISSN: 0272-4979

In this paper we study the diffusion approximation of a swarming model given by a systemof interacting Langevin equations with nonlinear friction. The diffusion approximationrequires the calculation of the drift and diffusion coefficients that are given as averages ofsolutions to appropriate Poisson equations. We present a new numerical method for computingthese coefficients that is based on the calculation of the eigenvalues and eigenfunctionsof a Schr¨odinger operator. These theoretical results are supported by numerical simulationsshowcasing the efficiency of the method.

Thompson AB, Gomes SN, Pavliotis,
et al., 2016, Stabilising falling liquid film flows using feedback control, *Physics of Fluids*, Vol: 28, ISSN: 1089-7666

Falling liquid films become unstable due to inertial effects when the fluid layer is sufficiently thick or the slopesufficiently steep. This free surface flow of a single fluid layer has industrial applications including coating andheat transfer, which benefit from smooth and wavy interfaces, respectively. Here we discuss how the dynamicsof the system are altered by feedback controls based on observations of the interface height, and supplied tothe system via the perpendicular injection and suction of fluid through the wall. In this study, we modelthe system using both Benney and weighted-residual models that account for the fluid injection throughthe wall. We find that feedback using injection and suction is a remarkably effective control mechanism:the controls can be used to drive the system towards arbitrary steady states and travelling waves, and thequalitative effects are independent of the details of the flow modelling. Furthermore, we show that the systemcan still be successfully controlled when the feedback is applied via a set of localised actuators and only asmall number of system observations are available, and that this is possible using both static (where thecontrols are based on only the most recent set of observations) and dynamic (where the controls are based onan approximation of the system which evolves over time) control schemes. This study thus provides a solidtheoretical foundation for future experimental realisations of the active feedback control of falling liquid films.

Krumscheid S, Pradas M, Pavliotis GA,
et al., 2015, Data-driven coarse graining in action: Modeling and prediction of complex systems, *PHYSICAL REVIEW E*, Vol: 92, ISSN: 2470-0045

Ottobre M, Pavliotis GA, Pravda-Starov K, 2015, Some remarks on degenerate hypoelliptic Ornstein-Uhlenbeck operators, *JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS*, Vol: 429, Pages: 676-712, ISSN: 0022-247X

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Gomes SN, Pradas M, Kalliadasis S,
et al., 2015, Controlling spatiotemporal chaos in active dissipative-dispersive nonlinear systems, *Physical Review E - Statistical, Nonlinear, and Soft Matter Physics*, Vol: 92, ISSN: 1539-3755

We present an alternative methodology for the stabilization and control of infinite-dimensional dynamical systems exhibiting low-dimensional spatiotemporal chaos. We show that with an appropriate choice of time-dependent controls we are able to stabilize and/or control all stable or unstable solutions, including steady solutions, traveling waves (single and multipulse ones or bound states), and spatiotemporal chaos. We exemplify our methodology with the generalized Kuramoto-Sivashinsky equation, a paradigmatic model of spatiotemporal chaos, which is known to exhibit a rich spectrum of wave forms and wave transitions and a rich variety of spatiotemporal structures.

Kalliadasis S, Krumscheid S, Pavliotis GA, 2015, A new framework for extracting coarse-grained models from time series with multiscale structure, *Journal of Computational Physics*, Vol: 296, Pages: 314-328, ISSN: 1090-2716

In many applications it is desirable to infer coarse-grained models from observational data. The observed process often corresponds only to a few selected degrees of freedom of a high-dimensional dynamical system with multiple time scales. In this work we consider the inference problem of identifying an appropriate coarse-grained model from a single time series of a multiscale system. It is known that estimators such as the maximum likelihood estimator or the quadratic variation of the path estimator can be strongly biased in this setting. Here we present a novel parametric inference methodology for problems with linear parameter dependency that does not suffer from this drawback. Furthermore, we demonstrate through a wide spectrum of examples that our methodology can be used to derive appropriate coarse-grained models from time series of partial observations of a multiscale system in an effective and systematic fashion.

Schmuck M, Pradas M, Pavliotis GA,
et al., 2015, A new mode reduction strategy for the generalized Kuramoto-Sivashinsky equation, *IMA JOURNAL OF APPLIED MATHEMATICS*, Vol: 80, Pages: 273-301, ISSN: 0272-4960

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- Citations: 4

Duncan AB, Elliott CM, Pavliotis GA,
et al., 2015, A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces, *JOURNAL OF NONLINEAR SCIENCE*, Vol: 25, Pages: 389-449, ISSN: 0938-8974

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- Citations: 2

Joubaud R, Pavliotis GA, Stoltz G, 2015, Langevin Dynamics with Space-Time Periodic Nonequilibrium Forcing, *JOURNAL OF STATISTICAL PHYSICS*, Vol: 158, Pages: 1-36, ISSN: 0022-4715

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Hartmann C, Latorre JC, Zhang W,
et al., 2014, Optimal control of multiscale systems using reduced-order models, *Journal of Computational Dynamics*, Vol: 1, Pages: 279-306

© American Institute of Mathematical Sciences. We study optimal control of diffusions with slow and fast variables and address a question raised by practitioners: is it possible to first eliminate the fast variables before solving the optimal control problem and then use the optimal control computed from the reduced-order model to control the original, high-dimensional system? The strategy "first reduce, then optimize"-rather than "first optimize, then reduce"-is motivated by the fact that solving optimal control problems for high-dimensional multiscale systems is numerically challenging and often computationally prohibitive. We state suffcient and necessary conditions, under which the "first reduce, then control" strategy can be employed and discuss when it should be avoided. We further give numerical examples that illustrate the "first reduce, then optmize" approach and discuss possible pitfalls.

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