General Doubly Stochastic Maximum Principle and Its Applications to Optimal Control of SPDEs

In this paper, we prove the necessary and sufficient maximum principles (NSMPs in short) for the optimal control of systems described by a quasilinear stochastic heat equation within convex control domains, which all the coefficients contain control variables. For that, the optimal control problem of fully coupled forward-backward doubly stochastic system is studied. We apply our NSMPs to treat a kind of forward-backward doubly stochastic linear quadratic optimal control problems and an example of optimal control of stochastic partial differential equations (SPDEs in short) as well.Comment: arXiv admin note: text overlap with arXiv:1005.412

A Moment and Sum-of-Squares Extension of Dual Dynamic Programming with Application to Nonlinear Energy Storage Problems

We present a finite-horizon optimization algorithm that extends the established concept of Dual Dynamic Programming (DDP) in two ways. First, in contrast to the linear costs, dynamics, and constraints of standard DDP, we consider problems in which all of these can be polynomial functions. Second, we allow the state trajectory to be described by probability distributions rather than point values, and return approximate value functions fitted to these. The algorithm is in part an adaptation of sum-of-squares techniques used in the approximate dynamic programming literature. It alternates between a forward simulation through the horizon, in which the moments of the state distribution are propagated through a succession of single-stage problems, and a backward recursion, in which a new polynomial function is derived for each stage using the moments of the state as fixed data. The value function approximation returned for a given stage is the point-wise maximum of all polynomials derived for that stage. This contrasts with the piecewise affine functions derived in conventional DDP. We prove key convergence properties of the new algorithm, and validate it in simulation on two case studies related to the optimal operation of energy storage devices with nonlinear characteristics. The first is a small borehole storage problem, for which multiple value function approximations can be compared. The second is a larger problem, for which conventional discretized dynamic programming is intractable.Comment: 33 pages, 9 figure

Reversible thermal diode and energy harvester with a superconducting quantum interference single-electron transistor

The density of states of proximitized normal nanowires interrupting superconducting rings can be tuned by the magnetic flux piercing the loop. Using these as the contacts of a single-electron transistor allows to control the energetic mirror asymmetry of the conductor, this way introducing rectification properties. In particular, we show that the system works as a diode that rectifies both charge and heat currents and whose polarity can be reversed by the magnetic field and a gate voltage. We emphasize the role of dissipation at the island. The coupling to substrate phonons enhances the effect and furthermore introduces a channel for phase tunable conversion of heat exchanged with the environment into electrical current.Comment: 5 pages, 4 figures. Accepted versio

Unified Systems of FB-SPDEs/FB-SDEs with Jumps/Skew Reflections and Stochastic Differential Games

We study four systems and their interactions. First, we formulate a unified system of coupled forward-backward stochastic partial differential equations (FB-SPDEs) with Levy jumps, whose drift, diffusion, and jump coefficients may involve partial differential operators. A solution to the FB-SPDEs is defined by a 4-tuple general dimensional random vector-field process evolving in time together with position parameters over a domain (e.g., a hyperbox or a manifold). Under an infinite sequence of generalized local linear growth and Lipschitz conditions, the well-posedness of an adapted 4-tuple strong solution is proved over a suitably constructed topological space. Second, we consider a unified system of FB-SDEs, a special form of the FB-SPDEs, however, with skew boundary reflections. Under randomized linear growth and Lipschitz conditions together with a general completely-S condition on reflections, we prove the well-posedness of an adapted 6-tuple weak solution with boundary regulators to the FB-SDEs by the Skorohod problem and an oscillation inequality. Particularly, if the spectral radii in some sense for reflection matrices are strictly less than the unity, an adapted 6-tuple strong solution is concerned. Third, we formulate a stochastic differential game (SDG) with general number of players based on the FB-SDEs. By a solution to the FB-SPDEs, we get a solution to the FB-SDEs under a given control rule and then obtain a Pareto optimal Nash equilibrium policy process to the SDG. Fourth, we study the applications of the FB-SPDEs/FB-SDEs in queueing systems and quantum statistics while we use them to motivate the SDG.Comment: 58 pages, 6 figures, invited talks and plenary talks at a number of conferences and workshop

Convergence and coupling for spin glasses and hard spheres

We discuss convergence and coupling of Markov chains, and present general relations between the transfer matrices describing these two processes. We then analyze a recently developed local-patch algorithm, which computes rigorous upper bound for the coupling time of a Markov chain for non-trivial statistical-mechanics models. Using the coupling from the past protocol, this allows one to exactly sample the underlying equilibrium distribution. For spin glasses in two and three spatial dimensions, the local-patch algorithm works at lower temperatures than previous exact-sampling methods. We discuss variants of the algorithm which might allow one to reach, in three dimensions, the spin-glass transition temperature. The algorithm can be adapted to hard-sphere models. For two-dimensional hard disks, the algorithm allows us to draw exact samples at higher densities than previously possible.Comment: 22 pages, 19 figures, python cod

Symplectic Reconstruction of Data for Heat and Wave Equations

This report concerns the inverse problem of estimating a spacially dependent coefficient of a partial differential equation from observations of the solution at the boundary. Such a problem can be formulated as an optimal control problem with the coefficient as the control variable and the solution as state variable. The heat or the wave equation is here considered as state equation. It is well known that such inverse problems are ill-posed and need to be regularized. The powerful Hamilton-Jacobi theory is used to construct a simple and general method where the first step is to analytically regularize the Hamiltonian; next its Hamiltonian system, a system of nonlinear partial differential equations, is solved with the Newton method and a sparse Jacobian

Optimal Estimation of Dynamically Evolving Diffusivities

The augmented, iterated Kalman smoother is applied to system identification for inverse problems in evolutionary differential equations. In the augmented smoother, the unknown, time-dependent coefficients are included in the state vector, and have a stochastic component. At each step in the iteration, the estimate of the time evolution of the coefficients is linear. We update the slowly varying mean temperature and conductivity by averaging the estimates of the Kalman smoother. Applications include the estimation of anomalous diffusion coefficients in turbulent fluids and the plasma rotation velocity in plasma tomography

Allostery and Kinetic Proofreading

Kinetic proofreading is an error correction mechanism present in the processes of the central dogma and beyond, and typically requires the free energy of nucleotide hydrolysis for its operation. Though the molecular players of many biological proofreading schemes are known, our understanding of how energy consumption is managed to promote fidelity remains incomplete. In our work, we introduce an alternative conceptual scheme called 'the piston model of proofreading' where enzyme activation through hydrolysis is replaced with allosteric activation achieved through mechanical work performed by a piston on regulatory ligands. Inspired by Feynman's ratchet and pawl mechanism, we consider a mechanical engine designed to drive the piston actions powered by a lowering weight, whose function is analogous to that of ATP synthase in cells. Thanks to its mechanical design, the piston model allows us to tune the 'knobs' of the driving engine and probe the graded changes and trade-offs between speed, fidelity and energy dissipation. It provides an intuitive explanation of the conditions necessary for optimal proofreading and reveals the unexpected capability of allosteric molecules to beat the Hopfield limit of fidelity by leveraging the diversity of states available to them. The framework that we built for the piston model can also serve as a basis for additional studies of driven biochemical systems

Minimum-Time Cavity Optomechanical Cooling

Optomechanical cooling is a prerequisite for many exotic applications promised by modern quantum technology and it is crucial to achieve it in short times, in order to minimize the undesirable effects of the environment. We formulate cavity optomechanical cooling as a minimum-time optimal control problem on anti-de Sitter space of appropriate dimension and use the Legendre pseudospectral optimization method to find the minimum time and the corresponding optimal control, for various values of the maximum coupling rate between the cavity field and the mechanical resonator. The present framework can also be applied to create optomechanical entanglement in minimum time and to improve the efficiency of an optomechanical quantum heat engine

On the mean field games with common noise and the McKean-Vlasov SPDEs

We formulate the MFG limit for $N$ interacting agents with a common noise as a single quasi-linear deterministic infinite-dimensional partial differential second order backward equation. We prove that any its (regular enough) solution provides an $1/N$-Nash-equilibrium profile for the initial $N$-player game. We use the method of stochastic characteristics to provide the link with the basic models of MFG with a major player. We develop two auxiliary theories of independent interest: sensitivity and regularity analysis for the McKean-Vlasov SPDEs and the $1/N$-convergence rate for the propagation of chaos property of interacting diffusions