American Institute of Mathematical Sciences

Advanced
June  2020, 7(1): 1-33. doi: 10.3934/jcd.2020001

Model reduction of controlled Fokker–Planck and Liouville–von Neumann equations

Peter Benner 1, , Tobias Breiten 2, , Carsten Hartmann 3,, and  Burkhard Schmidt 4,

1. 

Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
2. 

Institut für Mathematik, Karl-Franzens-Universität, Heinrichstr. 36/Ⅲ, A-8010 Graz, Austria
3. 

Institut für Mathematik, Brandenburgische Technische Universität Cottbus-Senftenberg, Konrad-Wachsmann-Allee 1, D-03046 Cottbus, Germany
4. 

Institut für Mathematik, Freie Universität Berlin, Arnimallee 6, D-14195 Berlin, Germany

* Corresponding author

Received  May 2018 Revised  June 2019 Published  November 2019

Fund Project: This work was partly supported by the Einstein Center for Mathematics Berlin (ECMath)

We study and compare two different model reduction techniques for bilinear systems, specifically generalized balancing and $ \mathcal{H}_2 $-based model reduction, and apply it to semi-discretized controlled Fokker-Planck and Liouville–von Neumann equations. For this class of transport equations, the control enters the dynamics as an advection term that leads to the bilinear form. A specific feature of the systems is that they are stable, but not asymptotically stable, and we discuss aspects regarding structure and stability preservation in some depth as these aspects are particularly relevant for the equations of interest. Another focus of this article is on the numerical implementation and a thorough comparison of the aforementioned model reduction methods.

Keywords: Bilinear systems, $ \mathcal{H}_{2} $ model reduction, balanced truncation, generalized Lyapunov equations, generalized Sylvester equations, Fokker-Planck equation, Liouville–von Neumann equations.
Mathematics Subject Classification: Primary: 93A15, 93B40; Secondary: 93C10, 93C70.
Citation: Peter Benner, Tobias Breiten, Carsten Hartmann, Burkhard Schmidt. Model reduction of controlled Fokker–Planck and Liouville–von Neumann equations. Journal of Computational Dynamics, 2020, 7 (1) : 1-33. doi: 10.3934/jcd.2020001
References:
[1]

M. I. Ahmad, U. Baur and P. Benner, Implicit Volterra series interpolation for model reduction of bilinear systems, J. Comput. Appl. Math., 316 (2017), 15-28.  doi: 10.1016/j.cam.2016.09.048.

[2]

S. Al-Baiyat and M. Bettayeb, A new model reduction scheme for k-power bilinear systems, Proc. 32nd IEEE Conf. Decis. Control, 32 (1993), 22-27.  doi: 10.1109/CDC.1993.325196.

[3]

I. Andrianov and P. Saalfrank, Theoretical study of vibration–phonon coupling of H adsorbed on a Si(100) surface, J. Chem. Phys., 124 (2006). doi: 10.1063/1.2161191.

[4]

M. Annunziato and A. Borzi, A Fokker–Planck control framework for multidimensional stochastic processes, J. Comput. Appl. Math., 237 (2013), 487-507.  doi: 10.1016/j.cam.2012.06.019.

[5]

A. Antoulas, Approximation of Large-Scale Dynamical Systems, Advances in Design Control, 6, SIAM, Philadelphia, 2005. doi: 10.1137/1.9780898718713.

[6]

Z. Bai and D. Skoogh, A projection method for model reduction of bilinear dynamical systems, Linear Algebra Appl., 415 (2006), 406-425.  doi: 10.1016/j.laa.2005.04.032.

[7]

U. Baur, P. Benner and L. Feng, Model order reduction for linear and nonlinear systems: A system-theoretic perspective, Arch. Comput. Methods Eng., 21 (2014), 331-358.  doi: 10.1007/s11831-014-9111-2.

[8]

S. Becker and C. Hartmann, Infinite-dimensional bilinear and stochastic balanced truncation with explicit error bounds, Math. Control Signals Systems, 31 (2019), 1-37.  doi: 10.1007/s00498-019-0234-8.

[9]

P. Benner and T. Breiten, Interpolation-based $\mathcal{H}_2$-model reduction of bilinear control systems, SIAM J. Matrix Anal. Appl., 33 (2012), 859-885.  doi: 10.1137/110836742.

[10]

P. Benner and T. Damm, Lyapunov equations, energy functionals, and model order reduction, SIAM J. Control Optim., 49 (2011), 686-711.  doi: 10.1137/09075041X.

[11]

P. Benner, T. Damm, M. Redmann and Y. R. Rodriguez Cruz, Positive operators and stable truncation, Linear Algebra Appl., 498 (2016), 74-87.  doi: 10.1016/j.laa.2014.12.005.

[12]

P. Benner, T. Damm and Y. R. Rodriguez Cruz, Dual pairs of generalized Lyapunov inequalities and balanced truncation of stochastic linear systems, IEEE Trans. Automat. Control, 62 (2017), 782-791.  doi: 10.1109/TAC.2016.2572881.

[13]

P. Benner, S. Gugercin and K. Willcox, A survey of projection-based model reduction methods for parametric dynamical systems, SIAM Rev., 57 (2015), 483-531.  doi: 10.1137/130932715.

[14]

P. Benner, P. Kürschner and J. Saak, Efficient handling of complex shift parameters in the low-rank cholesky factor ADI method, Numer. Algorithms, 62 (2013), 225-251.  doi: 10.1007/s11075-012-9569-7.

[15]

N. Berglund, Kramers' law: Validity, derivations and generalisations, Markov Process. Related Fields, 19 (2013), 459-490. 

[16]

C. Boess, A. Lawless, N. Nichols and A. Bunse-Gerstner, State estimation using model order reduction for unstable systems, Computers & Fluids, 46 (2011), 155-160.  doi: 10.1016/j.compfluid.2010.11.033.

[17]

T. Breiten and T. Damm, Krylov subspace methods for model order reduction of bilinear control systems, Systems Control Lett., 59 (2010), 443-450.  doi: 10.1016/j.sysconle.2010.06.003.

[18]

T. Breiten, K. Kunisch and L. Pfeiffer, Control strategies for the Fokker-Planck equation, ESAIM Control Optim. Calc. Var., 24 (2018), 741-763.  doi: 10.1051/cocv/2017046.

[19]

H.-P. Breuer, W. Huber and F. Petruccione, Stochastic wave-function method versus density matrix: A numerical comparison, Comput. Phys. Comm., 104 (1997), 46-58.  doi: 10.1016/S0010-4655(97)00050-7.

[20] H.-P. Breuer and F. Petruccione, The Theory of Open Quantum Systems, Oxford University Press, New York, 2002.  doi: 10.1093/acprof:oso/9780199213900.001.0001.
[21]

M. Condon and R. Ivanov, Empirical balanced truncation of nonlinear systems, J. Nonlinear Sci., 14 (2004), 405-414.  doi: 10.1007/s00332-004-0617-5.

[22]

M. Condon and R. Ivanov, Nonlinear systems – Algebraic Gramians and model reduction, COMPEL, 24 (2005), 202-219.  doi: 10.1108/03321640510571147.

[23]

T. Damm, Direct methods and ADI-preconditioned Krylov subspace methods for generalized Lyapunov equations, Numer. Linear Algebra Appl., 15 (2008), 853-871.  doi: 10.1002/nla.603.

[24]

N. de Souza, Pulling on single molecules, Nature Meth., 9 (2012), 873-877.  doi: 10.1038/nmeth.2149.

[25]

G. Flagg, Interpolation Methods for the Model Reduction of Bilinear Systems, Ph.D thesis, Virginia Tech, 2012.

[26]

G. Flagg and S. Gugercin, Multipoint volterra series interpolation and $\mathcal{H}_2$ optimal model reduction of bilinear systems, SIAM J. Matrix Anal. Appl., 36 (2015), 549-579.  doi: 10.1137/130947830.

[27]

V. Gaitsgory, Suboptimization of singularly perturbed control systems, SIAM J. Control Optim., 30 (1992), 1228-1249.  doi: 10.1137/0330065.

[28]

G. Grammel, Averaging of singularly perturbed systems, Nonlinear Anal., 28 (1997), 1851-1865.  doi: 10.1016/S0362-546X(95)00243-O.

[29]

W. Gray and J. P. Mesko, Observability functions for linear and nonlinear systems, Systems Control Lett., 38 (1999), 99-113.  doi: 10.1016/S0167-6911(99)00051-1.

[30]

S. Gugercin, A. Antoulas and S. Beattie, $\mathcal{H}_2$ model reduction for large-scale dynamical systems, SIAM J. Matrix Anal. Appl., 30 (2008), 609-638.  doi: 10.1137/060666123.

[31]

C. Hartmann, B. Schäfer-Bung and A. Zueva, Balanced averaging of bilinear systems with applications to stochastic control, SIAM J. Control Optim., 51 (2013), 2356-2378.  doi: 10.1137/100796844.

[32]

C. Hartmann and C. Schütte, Efficient rare event simulation by optimal nonequilibrium forcing, J. Stat. Mech. Theor. Exp., 2012 (2012). doi: 10.1088/1742-5468/2012/11/P11004.

[33]

C. Hartmann, V. Vulcanov and C. Schütte, Balanced truncation of linear second-order systems: A Hamiltonian approach, Multiscale Model. Simul., 8 (2010), 1348-1367.  doi: 10.1137/080732717.

[34]

G. Hummer and A. Szabo, Free energy profiles from single-molecule pulling experiments, Proc. Natl. Acad. Sci. USA, 107 (2010), 21441-21446.  doi: 10.1073/pnas.1015661107.

[35]

A. Isidori, Direct construction of minimal bilinear realizations from nonlinear input-output maps, IEEE Trans. Automatic Control, 18 (1973), 626-631.  doi: 10.1109/tac.1973.1100424.

[36]

S. Lall, J. Marsden and S. Glavaški, A subspace approach to balanced truncation for model reduction of nonlinear control systems, Internat. J. Robust Nonlinear Control, 12 (2002), 519-535.  doi: 10.1002/rnc.657.

[37]

J. C. Latorre, P. Metzner, C. Hartmann and C. Schütte, A structure-preserving numerical discretization of reversible diffusions, Commun. Math. Sci., 9 (2011), 1051-1072.  doi: 10.4310/CMS.2011.v9.n4.a6.

[38]

A. S. Lawless, N. K. Nichols, C. Boess and A. Bunse-Gerstner, Using model reduction methods within incremental four-dimensional variational data assimilation, Monthly Weather Review, 136 (2008), 1511-1522.  doi: 10.1175/2007MWR2103.1.

[39]

C. Le Bris, Y. Maday and G. Turinici, Towards efficient numerical approaches for quantum control, in Quantum Control: Mathematical and Cumerical Cchallenges, CRM Proc. Lecture Notes, 33, Amer. Math. Soc., Providence, RI, 2003.

[40]

T. Lelivre and G. Stoltz, Partial differential equations and stochastic methods in molecular dynamics, Acta Numer., 25 (2016), 681-880.  doi: 10.1017/S0962492916000039.

[41]

J.-R. Li and J. White, Low-rank solution of Lyapunov equations, SIAM Rev., 46 (2004), 693-713.  doi: 10.1137/S0036144504443389.

[42]

Y. Lin, L. Bao and Y. Wei, Order reduction of bilinear MIMO dynamical systems using new block Krylov subspaces, Comput. Math. Appl., 58 (2009), 1093-1102.  doi: 10.1016/j.camwa.2009.07.039.

[43]

G. Lindblad, On the generators of quantum dynamical semigroups, Comm. Math. Phys., 48 (1976), 119-130.  doi: 10.1007/BF01608499.

[44]

L. Meier and D. Luenberger, Approximation of linear constant systems, IEEE Transactions on Automatic Control, 12 (1967), 585-588.  doi: 10.1109/TAC.1967.1098680.

[45]

W. E. Moerner, Single-molecule spectroscopy, imaging, and photocontrol: Foundations for super-resolution microscopy (Nobel Lecture), Rev. Mod. Phys., 87 (2015), 1183-1212.  doi: 10.1002/anie.201501949.

[46]

M. Mohammadi, Analysis of discretization schemes for Fokker-Planck equations and related optimality systems, Ph.D thesis, Universität Würzburg, 2015.

[47]

B. Moore, Principal component analysis in linear system: Controllability, observability and model reduction, IEEE Trans. Automat. Control, 26 (1981), 17-32.  doi: 10.1109/TAC.1981.1102568.

[48]

R. O'Malley, Singular Perturbation Methods for Ordinary Differential Equations, Applied Mathematical Sciences, 89, Springer-Verlag, New York, 1991. doi: 10.1007/978-1-4612-0977-5.

[49]

M. Petreczky, R. Wisniewski and J. Leth, Moment matching for bilinear systems with nice selections, IFAC-PapersOnLine, 49 (2016), 838-843.  doi: 10.1016/j.ifacol.2016.10.270.

[50]

J. Phillips, Projection-based approaches for model reduction of weakly nonlinear, time-varying systems, IEEE T. Comput. Aided. D., 22 (2003), 171-187.  doi: 10.1109/TCAD.2002.806605.

[51]

M. Redmann and P. Benner, Singular perturbation approximation for linear systems with Lévy noise, Stoch. Dyn., 18 (2018). doi: 10.1142/S0219493718500338.

[52]

L. Rey-Bellet, Open classical systems, in Open Quantum Systems II: The Markovian Approach, Lecture Notes in Math., 1881, Springer, Berlin, 2006, 41–78. doi: 10.1007/3-540-33966-3_2.

[53] W. Rugh, Nonlinear System Theory, Johns Hopkins University Press, Baltimore, MD, 1981. 
[54]

B. Schäfer-Bung, C. Hartmann, B. Schmidt and C. Schütte, Dimension reduction by balanced truncation: Application to light-induced control of open quantum systems, J Chem. Phys., 135 (2011). doi: 10.1063/1.3605243.

[55]

B. Schmidt and C. Hartmann, Wavepacket: A MATLAB package for numerical quantum dynamics. Ⅱ: Open quantum systems and optimal control, Comput. Phys. Commun., 228 (2018), 229-244.  doi: 10.1016/j.cpc.2018.02.022.

[56]

B. Schmidt and U. Lorenz, Wavepacket: A MATLAB package for numerical quantum dynamics. Ⅰ: Closed quantum systems and discrete variable representations, Comput. Phys. Commun., 213 (2017), 223-234.  doi: 10.1016/j.cpc.2016.12.007.

[57]

T. Siu and M. Schetzen, Convergence of Volterra series representation and BIBO stability of bilinear systems, Internat. J. Systems Sci., 22 (1991), 2679-2684.  doi: 10.1080/00207729108910824.

[58]

A. K. Tiwari, K. B. Møller and N. E. Henriksen, Selective bond breakage within the HOD molecule using optimized femtosecond ultraviolet laser pulses, Phys. Rev. A, 78 (2008). doi: 10.1103/PhysRevA.78.065402.

[59]

A. Vigodner, Limits of singularly perturbed control problems with statistical dynamics of fast motions, SIAM J. Control Optim., 35 (1997), 1-28.  doi: 10.1137/S0363012994264207.

[60]

E. Wachspress, Iterative solution of the Lyapunov matrix equation, Appl. Math. Lett., 1 (1988), 87-90.  doi: 10.1016/0893-9659(88)90183-8.

[61]

F. Watbled, On singular perturbations for differential inclusions on the infinite interval, J. Math. Anal. Appl., 310 (2005), 362–378. doi: 10.1016/j.jmaa.2005.01.067.

[62]

U. Weiss, Quantum Dissipative Systems, Series in Modern Condensed Matter Physics, 10, World Scientific, Singapore, 1999. doi: 10.1142/9789812817877.

[63]

D. Wilson, Optimum solution of model-reduction problem, Proceedings of the Institution of Electrical Engineers, 117 (1970), 1161-1165.  doi: 10.1049/piee.1970.0227.

[64]

A. Zewail, Femtochemistry: Chemical reaction dynamics and their control, in Advances in Chemical Physics: Chemical Reactions and their Control on the Femtosecond Time Scale, 101, John Wiley & Sons, Hoboken, NJ, 1997,103–108. doi: 10.1002/9780470141601.ch1.

[65]

L. Zhang and J. Lam, On $H_2$ model reduction of bilinear systems, Automatica J. IFAC, 38 (2002), 205-216.  doi: 10.1016/S0005-1098(01)00204-7.

[66]

W. Zhang, H. Wang, C. Hartmann, M. Weber and C. Schütte, Applications of the cross-entropy method to importance sampling and optimal control of diffusions, SIAM J. Sci. Comput., 36 (2014), A2654–A2672. doi: 10.1137/14096493X.

show all references

References:
[1]

M. I. Ahmad, U. Baur and P. Benner, Implicit Volterra series interpolation for model reduction of bilinear systems, J. Comput. Appl. Math., 316 (2017), 15-28.  doi: 10.1016/j.cam.2016.09.048.

[2]

S. Al-Baiyat and M. Bettayeb, A new model reduction scheme for k-power bilinear systems, Proc. 32nd IEEE Conf. Decis. Control, 32 (1993), 22-27.  doi: 10.1109/CDC.1993.325196.

[3]

I. Andrianov and P. Saalfrank, Theoretical study of vibration–phonon coupling of H adsorbed on a Si(100) surface, J. Chem. Phys., 124 (2006). doi: 10.1063/1.2161191.

[4]

M. Annunziato and A. Borzi, A Fokker–Planck control framework for multidimensional stochastic processes, J. Comput. Appl. Math., 237 (2013), 487-507.  doi: 10.1016/j.cam.2012.06.019.

[5]

A. Antoulas, Approximation of Large-Scale Dynamical Systems, Advances in Design Control, 6, SIAM, Philadelphia, 2005. doi: 10.1137/1.9780898718713.

[6]

Z. Bai and D. Skoogh, A projection method for model reduction of bilinear dynamical systems, Linear Algebra Appl., 415 (2006), 406-425.  doi: 10.1016/j.laa.2005.04.032.

[7]

U. Baur, P. Benner and L. Feng, Model order reduction for linear and nonlinear systems: A system-theoretic perspective, Arch. Comput. Methods Eng., 21 (2014), 331-358.  doi: 10.1007/s11831-014-9111-2.

[8]

S. Becker and C. Hartmann, Infinite-dimensional bilinear and stochastic balanced truncation with explicit error bounds, Math. Control Signals Systems, 31 (2019), 1-37.  doi: 10.1007/s00498-019-0234-8.

[9]

P. Benner and T. Breiten, Interpolation-based $\mathcal{H}_2$-model reduction of bilinear control systems, SIAM J. Matrix Anal. Appl., 33 (2012), 859-885.  doi: 10.1137/110836742.

[10]

P. Benner and T. Damm, Lyapunov equations, energy functionals, and model order reduction, SIAM J. Control Optim., 49 (2011), 686-711.  doi: 10.1137/09075041X.

[11]

P. Benner, T. Damm, M. Redmann and Y. R. Rodriguez Cruz, Positive operators and stable truncation, Linear Algebra Appl., 498 (2016), 74-87.  doi: 10.1016/j.laa.2014.12.005.

[12]

P. Benner, T. Damm and Y. R. Rodriguez Cruz, Dual pairs of generalized Lyapunov inequalities and balanced truncation of stochastic linear systems, IEEE Trans. Automat. Control, 62 (2017), 782-791.  doi: 10.1109/TAC.2016.2572881.

[13]

P. Benner, S. Gugercin and K. Willcox, A survey of projection-based model reduction methods for parametric dynamical systems, SIAM Rev., 57 (2015), 483-531.  doi: 10.1137/130932715.

[14]

P. Benner, P. Kürschner and J. Saak, Efficient handling of complex shift parameters in the low-rank cholesky factor ADI method, Numer. Algorithms, 62 (2013), 225-251.  doi: 10.1007/s11075-012-9569-7.

[15]

N. Berglund, Kramers' law: Validity, derivations and generalisations, Markov Process. Related Fields, 19 (2013), 459-490. 

[16]

C. Boess, A. Lawless, N. Nichols and A. Bunse-Gerstner, State estimation using model order reduction for unstable systems, Computers & Fluids, 46 (2011), 155-160.  doi: 10.1016/j.compfluid.2010.11.033.

[17]

T. Breiten and T. Damm, Krylov subspace methods for model order reduction of bilinear control systems, Systems Control Lett., 59 (2010), 443-450.  doi: 10.1016/j.sysconle.2010.06.003.

[18]

T. Breiten, K. Kunisch and L. Pfeiffer, Control strategies for the Fokker-Planck equation, ESAIM Control Optim. Calc. Var., 24 (2018), 741-763.  doi: 10.1051/cocv/2017046.

[19]

H.-P. Breuer, W. Huber and F. Petruccione, Stochastic wave-function method versus density matrix: A numerical comparison, Comput. Phys. Comm., 104 (1997), 46-58.  doi: 10.1016/S0010-4655(97)00050-7.

[20] H.-P. Breuer and F. Petruccione, The Theory of Open Quantum Systems, Oxford University Press, New York, 2002.  doi: 10.1093/acprof:oso/9780199213900.001.0001.
[21]

M. Condon and R. Ivanov, Empirical balanced truncation of nonlinear systems, J. Nonlinear Sci., 14 (2004), 405-414.  doi: 10.1007/s00332-004-0617-5.

[22]

M. Condon and R. Ivanov, Nonlinear systems – Algebraic Gramians and model reduction, COMPEL, 24 (2005), 202-219.  doi: 10.1108/03321640510571147.

[23]

T. Damm, Direct methods and ADI-preconditioned Krylov subspace methods for generalized Lyapunov equations, Numer. Linear Algebra Appl., 15 (2008), 853-871.  doi: 10.1002/nla.603.

[24]

N. de Souza, Pulling on single molecules, Nature Meth., 9 (2012), 873-877.  doi: 10.1038/nmeth.2149.

[25]

G. Flagg, Interpolation Methods for the Model Reduction of Bilinear Systems, Ph.D thesis, Virginia Tech, 2012.

[26]

G. Flagg and S. Gugercin, Multipoint volterra series interpolation and $\mathcal{H}_2$ optimal model reduction of bilinear systems, SIAM J. Matrix Anal. Appl., 36 (2015), 549-579.  doi: 10.1137/130947830.

[27]

V. Gaitsgory, Suboptimization of singularly perturbed control systems, SIAM J. Control Optim., 30 (1992), 1228-1249.  doi: 10.1137/0330065.

[28]

G. Grammel, Averaging of singularly perturbed systems, Nonlinear Anal., 28 (1997), 1851-1865.  doi: 10.1016/S0362-546X(95)00243-O.

[29]

W. Gray and J. P. Mesko, Observability functions for linear and nonlinear systems, Systems Control Lett., 38 (1999), 99-113.  doi: 10.1016/S0167-6911(99)00051-1.

[30]

S. Gugercin, A. Antoulas and S. Beattie, $\mathcal{H}_2$ model reduction for large-scale dynamical systems, SIAM J. Matrix Anal. Appl., 30 (2008), 609-638.  doi: 10.1137/060666123.

[31]

C. Hartmann, B. Schäfer-Bung and A. Zueva, Balanced averaging of bilinear systems with applications to stochastic control, SIAM J. Control Optim., 51 (2013), 2356-2378.  doi: 10.1137/100796844.

[32]

C. Hartmann and C. Schütte, Efficient rare event simulation by optimal nonequilibrium forcing, J. Stat. Mech. Theor. Exp., 2012 (2012). doi: 10.1088/1742-5468/2012/11/P11004.

[33]

C. Hartmann, V. Vulcanov and C. Schütte, Balanced truncation of linear second-order systems: A Hamiltonian approach, Multiscale Model. Simul., 8 (2010), 1348-1367.  doi: 10.1137/080732717.

[34]

G. Hummer and A. Szabo, Free energy profiles from single-molecule pulling experiments, Proc. Natl. Acad. Sci. USA, 107 (2010), 21441-21446.  doi: 10.1073/pnas.1015661107.

[35]

A. Isidori, Direct construction of minimal bilinear realizations from nonlinear input-output maps, IEEE Trans. Automatic Control, 18 (1973), 626-631.  doi: 10.1109/tac.1973.1100424.

[36]

S. Lall, J. Marsden and S. Glavaški, A subspace approach to balanced truncation for model reduction of nonlinear control systems, Internat. J. Robust Nonlinear Control, 12 (2002), 519-535.  doi: 10.1002/rnc.657.

[37]

J. C. Latorre, P. Metzner, C. Hartmann and C. Schütte, A structure-preserving numerical discretization of reversible diffusions, Commun. Math. Sci., 9 (2011), 1051-1072.  doi: 10.4310/CMS.2011.v9.n4.a6.

[38]

A. S. Lawless, N. K. Nichols, C. Boess and A. Bunse-Gerstner, Using model reduction methods within incremental four-dimensional variational data assimilation, Monthly Weather Review, 136 (2008), 1511-1522.  doi: 10.1175/2007MWR2103.1.

[39]

C. Le Bris, Y. Maday and G. Turinici, Towards efficient numerical approaches for quantum control, in Quantum Control: Mathematical and Cumerical Cchallenges, CRM Proc. Lecture Notes, 33, Amer. Math. Soc., Providence, RI, 2003.

[40]

T. Lelivre and G. Stoltz, Partial differential equations and stochastic methods in molecular dynamics, Acta Numer., 25 (2016), 681-880.  doi: 10.1017/S0962492916000039.

[41]

J.-R. Li and J. White, Low-rank solution of Lyapunov equations, SIAM Rev., 46 (2004), 693-713.  doi: 10.1137/S0036144504443389.

[42]

Y. Lin, L. Bao and Y. Wei, Order reduction of bilinear MIMO dynamical systems using new block Krylov subspaces, Comput. Math. Appl., 58 (2009), 1093-1102.  doi: 10.1016/j.camwa.2009.07.039.

[43]

G. Lindblad, On the generators of quantum dynamical semigroups, Comm. Math. Phys., 48 (1976), 119-130.  doi: 10.1007/BF01608499.

[44]

L. Meier and D. Luenberger, Approximation of linear constant systems, IEEE Transactions on Automatic Control, 12 (1967), 585-588.  doi: 10.1109/TAC.1967.1098680.

[45]

W. E. Moerner, Single-molecule spectroscopy, imaging, and photocontrol: Foundations for super-resolution microscopy (Nobel Lecture), Rev. Mod. Phys., 87 (2015), 1183-1212.  doi: 10.1002/anie.201501949.

[46]

M. Mohammadi, Analysis of discretization schemes for Fokker-Planck equations and related optimality systems, Ph.D thesis, Universität Würzburg, 2015.

[47]

B. Moore, Principal component analysis in linear system: Controllability, observability and model reduction, IEEE Trans. Automat. Control, 26 (1981), 17-32.  doi: 10.1109/TAC.1981.1102568.

[48]

R. O'Malley, Singular Perturbation Methods for Ordinary Differential Equations, Applied Mathematical Sciences, 89, Springer-Verlag, New York, 1991. doi: 10.1007/978-1-4612-0977-5.

[49]

M. Petreczky, R. Wisniewski and J. Leth, Moment matching for bilinear systems with nice selections, IFAC-PapersOnLine, 49 (2016), 838-843.  doi: 10.1016/j.ifacol.2016.10.270.

[50]

J. Phillips, Projection-based approaches for model reduction of weakly nonlinear, time-varying systems, IEEE T. Comput. Aided. D., 22 (2003), 171-187.  doi: 10.1109/TCAD.2002.806605.

[51]

M. Redmann and P. Benner, Singular perturbation approximation for linear systems with Lévy noise, Stoch. Dyn., 18 (2018). doi: 10.1142/S0219493718500338.

[52]

L. Rey-Bellet, Open classical systems, in Open Quantum Systems II: The Markovian Approach, Lecture Notes in Math., 1881, Springer, Berlin, 2006, 41–78. doi: 10.1007/3-540-33966-3_2.

[53] W. Rugh, Nonlinear System Theory, Johns Hopkins University Press, Baltimore, MD, 1981. 
[54]

B. Schäfer-Bung, C. Hartmann, B. Schmidt and C. Schütte, Dimension reduction by balanced truncation: Application to light-induced control of open quantum systems, J Chem. Phys., 135 (2011). doi: 10.1063/1.3605243.

[55]

B. Schmidt and C. Hartmann, Wavepacket: A MATLAB package for numerical quantum dynamics. Ⅱ: Open quantum systems and optimal control, Comput. Phys. Commun., 228 (2018), 229-244.  doi: 10.1016/j.cpc.2018.02.022.

[56]

B. Schmidt and U. Lorenz, Wavepacket: A MATLAB package for numerical quantum dynamics. Ⅰ: Closed quantum systems and discrete variable representations, Comput. Phys. Commun., 213 (2017), 223-234.  doi: 10.1016/j.cpc.2016.12.007.

[57]

T. Siu and M. Schetzen, Convergence of Volterra series representation and BIBO stability of bilinear systems, Internat. J. Systems Sci., 22 (1991), 2679-2684.  doi: 10.1080/00207729108910824.

[58]

A. K. Tiwari, K. B. Møller and N. E. Henriksen, Selective bond breakage within the HOD molecule using optimized femtosecond ultraviolet laser pulses, Phys. Rev. A, 78 (2008). doi: 10.1103/PhysRevA.78.065402.

[59]

A. Vigodner, Limits of singularly perturbed control problems with statistical dynamics of fast motions, SIAM J. Control Optim., 35 (1997), 1-28.  doi: 10.1137/S0363012994264207.

[60]

E. Wachspress, Iterative solution of the Lyapunov matrix equation, Appl. Math. Lett., 1 (1988), 87-90.  doi: 10.1016/0893-9659(88)90183-8.

[61]

F. Watbled, On singular perturbations for differential inclusions on the infinite interval, J. Math. Anal. Appl., 310 (2005), 362–378. doi: 10.1016/j.jmaa.2005.01.067.

[62]

U. Weiss, Quantum Dissipative Systems, Series in Modern Condensed Matter Physics, 10, World Scientific, Singapore, 1999. doi: 10.1142/9789812817877.

[63]

D. Wilson, Optimum solution of model-reduction problem, Proceedings of the Institution of Electrical Engineers, 117 (1970), 1161-1165.  doi: 10.1049/piee.1970.0227.

[64]

A. Zewail, Femtochemistry: Chemical reaction dynamics and their control, in Advances in Chemical Physics: Chemical Reactions and their Control on the Femtosecond Time Scale, 101, John Wiley & Sons, Hoboken, NJ, 1997,103–108. doi: 10.1002/9780470141601.ch1.

[65]

L. Zhang and J. Lam, On $H_2$ model reduction of bilinear systems, Automatica J. IFAC, 38 (2002), 205-216.  doi: 10.1016/S0005-1098(01)00204-7.

[66]

W. Zhang, H. Wang, C. Hartmann, M. Weber and C. Schütte, Applications of the cross-entropy method to importance sampling and optimal control of diffusions, SIAM J. Sci. Comput., 36 (2014), A2654–A2672. doi: 10.1137/14096493X.

Figure 1.  Periodically perturbed quadruple-well potential (36) used in our FPE example
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Eigenvectors of the discretization matrix $ A $ for our FPE example, associated with the first four right eigenvalues $ \lambda $ for $ \beta = 4 $. Note that the eigenvector for $ \lambda = 0 $ (upper left panel) corresponds to the canonical density (35)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  $ {\mathcal H}_2 $ error versus reduced dimension for the FPE example for $ \beta = 4 $. Comparison of BT method, SP method, and H2 method. Values that are not shown are those for which the computed error has dropped below machine precision (see Sec. 6.4)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Time evolution of observables for the FPE example for $ \beta = 4 $ and for the control field given by Eq. (41) with $ t_0 = 150 $, $ \tau = 100 $, and $ a = 0.5 $: populations of the four quadrants of the $ x_1 $-$ x_2 $ plane for full ($ n = 2401 $) versus reduced dimensionality. From left to right: BT method, SP method, and H2 method
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Spectrum of the $ A $ matrix for the LvNE example for full versus reduced dimensionality. For relaxation rate $ \Gamma = 0.1 $ and temperature $ \Theta = 0.1 $. From left to right: BT method, SP method, and H2 method
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  $ {\mathcal H}_2 $ error versus reduced dimensionality $ d $ for the LvNE example. Simulation results for which the error has dropped below machine precision are considered numerical artifact and thus are not shown. Left: For various values of the relaxation rate $ \Gamma $ (for constant temperature, $ \Theta = 0.1 $). Right: For various values of the temperature $ \Theta $ (for constant relaxation, $ \Gamma = 0.1 $)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Time evolution of observables for the LvNE example for relaxation rate $ \Gamma = 0.1 $ and temperature $ \Theta = 0.1 $. The control field is given by Eq. (41) with $ a = 3 $, $ t_0 = 15 $, and $ \tau = 10 $. Populations of states localized in the left well, in the right well, and delocalized states over the barrier, for full ($ n = 441 $) versus reduced dimensionality. From left to right: BT method, SP method, and H2 method
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Lowest twelve eigenvalues (in magnitude) of the discretization matrix $ A $ of the FPE example for inverse temperature $ \beta = 4 $ showing three clusters of four members each. Comparison of full versus reduced dynamics using the BT and H2 method. Results for the SP method (not shown) are very close to those for the BT method. For all practical purposes, the reduced systems for $ d>100 $ are virtually indistinguishable from the full-rank system
BT method H2 method
full $ d=100 $ $ d=50 $ $ d=25 $ $ d=100 $ $ d=50 $ $ d=25 $
-0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000
-0.0037 -0.0037 -0.0037 -0.0037 -0.0037 -0.0037 -0.0037
-0.0073 -0.0073 -0.0073 -0.0074 -0.0073 -0.0073 -0.0074
-0.0118 -0.0118 -0.0118 -0.0118 -0.0118 -0.0118 -0.0118
-0.3266 -0.3265 -0.3260 -0.3264 -0.3265 -0.3255 -0.3263
-0.3303 -0.3297 -0.3294 -0.3504 -0.3298 -0.3298 -0.3629
-0.3358 -0.3353 -0.3423 -0.5455 -0.3349 -0.3432 -0.5450
-0.3447 -0.3447 -0.3432 -0.5582 -0.3445 -0.3432 -0.6058
-0.5421 -0.5422 -0.5434 -0.6083 -0.5412 -0.5435 -0.6336
-0.5453 -0.5455 -0.5606 -0.6487 -0.5450 -0.5622 -0.6676
-0.5666 -0.5657 -0.5867 -0.7683 -0.5663 -0.5888 -0.7791
-0.5948 -0.5951 -0.6107 -0.8003 -0.5951 -0.6192 -0.8052
Table Options

Download as excel

BT method H2 method
full $ d=100 $ $ d=50 $ $ d=25 $ $ d=100 $ $ d=50 $ $ d=25 $
-0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000
-0.0037 -0.0037 -0.0037 -0.0037 -0.0037 -0.0037 -0.0037
-0.0073 -0.0073 -0.0073 -0.0074 -0.0073 -0.0073 -0.0074
-0.0118 -0.0118 -0.0118 -0.0118 -0.0118 -0.0118 -0.0118
-0.3266 -0.3265 -0.3260 -0.3264 -0.3265 -0.3255 -0.3263
-0.3303 -0.3297 -0.3294 -0.3504 -0.3298 -0.3298 -0.3629
-0.3358 -0.3353 -0.3423 -0.5455 -0.3349 -0.3432 -0.5450
-0.3447 -0.3447 -0.3432 -0.5582 -0.3445 -0.3432 -0.6058
-0.5421 -0.5422 -0.5434 -0.6083 -0.5412 -0.5435 -0.6336
-0.5453 -0.5455 -0.5606 -0.6487 -0.5450 -0.5622 -0.6676
-0.5666 -0.5657 -0.5867 -0.7683 -0.5663 -0.5888 -0.7791
-0.5948 -0.5951 -0.6107 -0.8003 -0.5951 -0.6192 -0.8052
[1]

Yongkuan Cheng, Yaotian Shen. Generalized quasilinear Schrödinger equations with concave functions $ l(s^2) $. Discrete and Continuous Dynamical Systems, 2019, 39 (3) : 1311-1343. doi: 10.3934/dcds.2019056
[2]

Yang Liu, Chunyou Sun. Inviscid limit for the damped generalized incompressible Navier-Stokes equations on $ \mathbb{T}^2 $. Discrete and Continuous Dynamical Systems - S, 2021, 14 (12) : 4383-4408. doi: 10.3934/dcdss.2021124
[3]

Kwangseok Choe, Hyungjin Huh. Chern-Simons gauged sigma model into $ \mathbb{H}^2 $ and its self-dual equations. Discrete and Continuous Dynamical Systems, 2019, 39 (8) : 4613-4646. doi: 10.3934/dcds.2019189
[4]

Shui-Nee Chow, Wuchen Li, Haomin Zhou. Entropy dissipation of Fokker-Planck equations on graphs. Discrete and Continuous Dynamical Systems, 2018, 38 (10) : 4929-4950. doi: 10.3934/dcds.2018215
[5]

Martin Burger, Ina Humpert, Jan-Frederik Pietschmann. On Fokker-Planck equations with In- and Outflow of Mass. Kinetic and Related Models, 2020, 13 (2) : 249-277. doi: 10.3934/krm.2020009
[6]

Yu-Zhao Wang. $ \mathcal{W}$-Entropy formulae and differential Harnack estimates for porous medium equations on Riemannian manifolds. Communications on Pure and Applied Analysis, 2018, 17 (6) : 2441-2454. doi: 10.3934/cpaa.2018116
[7]

Dongyi Liu, Genqi Xu. Input-output $ L^2 $-well-posedness, regularity and Lyapunov stability of string equations on networks. Networks and Heterogeneous Media, 2022  doi: 10.3934/nhm.2022007
[8]

Yu-Ming Chu, Saima Rashid, Fahd Jarad, Muhammad Aslam Noor, Humaira Kalsoom. More new results on integral inequalities for generalized $ \mathcal{K} $-fractional conformable Integral operators. Discrete and Continuous Dynamical Systems - S, 2021, 14 (7) : 2119-2135. doi: 10.3934/dcdss.2021063
[9]

Lin Du, Yun Zhang. $\mathcal{H}_∞$ filtering for switched nonlinear systems: A state projection method. Journal of Industrial and Management Optimization, 2018, 14 (1) : 19-33. doi: 10.3934/jimo.2017035
[10]

Shengbing Deng. Construction solutions for Neumann problem with Hénon term in $ \mathbb{R}^2 $. Discrete and Continuous Dynamical Systems, 2019, 39 (4) : 2233-2253. doi: 10.3934/dcds.2019094
[11]

Hongjie Dong, Yan Guo, Timur Yastrzhembskiy. Kinetic Fokker-Planck and Landau equations with specular reflection boundary condition. Kinetic and Related Models, 2022, 15 (3) : 467-516. doi: 10.3934/krm.2022003
[12]

Xingyue Liang, Jianwei Xia, Guoliang Chen, Huasheng Zhang, Zhen Wang. $ \mathcal{H}_{\infty} $ control for fuzzy markovian jump systems based on sampled-data control method. Discrete and Continuous Dynamical Systems - S, 2021, 14 (4) : 1329-1343. doi: 10.3934/dcdss.2020368
[13]

Michael Herty, Christian Jörres, Albert N. Sandjo. Optimization of a model Fokker-Planck equation. Kinetic and Related Models, 2012, 5 (3) : 485-503. doi: 10.3934/krm.2012.5.485
[14]

Liejun Shen, Marco Squassina, Minbo Yang. Critical gauged Schrödinger equations in $ \mathbb{R}^2 $ with vanishing potentials. Discrete and Continuous Dynamical Systems, 2022  doi: 10.3934/dcds.2022059
[15]

James Montaldi, Amna Shaddad. Generalized point vortex dynamics on $ \mathbb{CP} ^2 $. Journal of Geometric Mechanics, 2019, 11 (4) : 601-619. doi: 10.3934/jgm.2019030
[16]

Xinliang An, Avy Soffer. Fermi's golden rule and $ H^1 $ scattering for nonlinear Klein-Gordon equations with metastable states. Discrete and Continuous Dynamical Systems, 2020, 40 (1) : 331-373. doi: 10.3934/dcds.2020013
[17]

Joackim Bernier. Bounds on the growth of high discrete Sobolev norms for the cubic discrete nonlinear Schrödinger equations on $ h\mathbb{Z} $. Discrete and Continuous Dynamical Systems, 2019, 39 (6) : 3179-3195. doi: 10.3934/dcds.2019131
[18]

Qiang Tu. A class of prescribed shifted Gauss curvature equations for horo-convex hypersurfaces in $ \mathbb{H}^{n+1} $. Discrete and Continuous Dynamical Systems, 2021, 41 (11) : 5397-5407. doi: 10.3934/dcds.2021081
[19]

Sugata Gangopadhyay, Goutam Paul, Nishant Sinha, Pantelimon Stǎnicǎ. Generalized nonlinearity of $ S$-boxes. Advances in Mathematics of Communications, 2018, 12 (1) : 115-122. doi: 10.3934/amc.2018007
[20]

Lihong Zhang, Wenwen Hou, Bashir Ahmad, Guotao Wang. Radial symmetry for logarithmic Choquard equation involving a generalized tempered fractional $ p $-Laplacian. Discrete and Continuous Dynamical Systems - S, 2021, 14 (10) : 3851-3863. doi: 10.3934/dcdss.2020445

 Impact Factor: 

Tools

Metrics

  • PDF downloads (334)
  • HTML views (505)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy