2,345 research outputs found
An Optimal Control Derivation of Nonlinear Smoothing Equations
The purpose of this paper is to review and highlight some connections between
the problem of nonlinear smoothing and optimal control of the Liouville
equation. The latter has been an active area of recent research interest owing
to work in mean-field games and optimal transportation theory. The nonlinear
smoothing problem is considered here for continuous-time Markov processes. The
observation process is modeled as a nonlinear function of a hidden state with
an additive Gaussian measurement noise. A variational formulation is described
based upon the relative entropy formula introduced by Newton and Mitter. The
resulting optimal control problem is formulated on the space of probability
distributions. The Hamilton's equation of the optimal control are related to
the Zakai equation of nonlinear smoothing via the log transformation. The
overall procedure is shown to generalize the classical Mortensen's minimum
energy estimator for the linear Gaussian problem.Comment: 7 pages, 0 figures, under peer reviewin
Quadratic Mean Field Games
Mean field games were introduced independently by J-M. Lasry and P-L. Lions,
and by M. Huang, R.P. Malham\'e and P. E. Caines, in order to bring a new
approach to optimization problems with a large number of interacting agents.
The description of such models split in two parts, one describing the evolution
of the density of players in some parameter space, the other the value of a
cost functional each player tries to minimize for himself, anticipating on the
rational behavior of the others.
Quadratic Mean Field Games form a particular class among these systems, in
which the dynamics of each player is governed by a controlled Langevin equation
with an associated cost functional quadratic in the control parameter. In such
cases, there exists a deep relationship with the non-linear Schr\"odinger
equation in imaginary time, connexion which lead to effective approximation
schemes as well as a better understanding of the behavior of Mean Field Games.
The aim of this paper is to serve as an introduction to Quadratic Mean Field
Games and their connexion with the non-linear Schr\"odinger equation, providing
to physicists a good entry point into this new and exciting field.Comment: 62 pages, 4 figure
Approximated Lax Pairs for the Reduced Order Integration of Nonlinear Evolution Equations
A reduced-order model algorithm, called ALP, is proposed to solve nonlinear
evolution partial differential equations. It is based on approximations of
generalized Lax pairs. Contrary to other reduced-order methods, like Proper
Orthogonal Decomposition, the basis on which the solution is searched for
evolves in time according to a dynamics specific to the problem. It is
therefore well-suited to solving problems with progressive front or wave
propagation. Another difference with other reduced-order methods is that it is
not based on an off-line / on-line strategy. Numerical examples are shown for
the linear advection, KdV and FKPP equations, in one and two dimensions
On the stochastic Strichartz estimates and the stochastic nonlinear Schr\"odinger equation on a compact riemannian manifold
We prove the existence and the uniqueness of a solution to the stochastic
NSLE on a two-dimensional compact riemannian manifold. Thus we generalize a
recent work by Burq, G\'erard and Tzvetkov in the deterministic setting, and a
series of papers by de Bouard and Debussche, who have examined similar
questions in the case of the flat euclidean space with random perturbation. We
prove the existence and the uniqueness of a local maximal solution to
stochastic nonlinear Schr\"odinger equations with multiplicative noise on a
compact d-dimensional riemannian manifold. Under more regularity on the noise,
we prove that the solution is global when the nonlinearity is of defocusing or
of focusing type, d=2 and the initial data belongs to the finite energy space.
Our proof is based on improved stochastic Strichartz inequalities
Extreme event quantification in dynamical systems with random components
A central problem in uncertainty quantification is how to characterize the
impact that our incomplete knowledge about models has on the predictions we
make from them. This question naturally lends itself to a probabilistic
formulation, by making the unknown model parameters random with given
statistics. Here this approach is used in concert with tools from large
deviation theory (LDT) and optimal control to estimate the probability that
some observables in a dynamical system go above a large threshold after some
time, given the prior statistical information about the system's parameters
and/or its initial conditions. Specifically, it is established under which
conditions such extreme events occur in a predictable way, as the minimizer of
the LDT action functional. It is also shown how this minimization can be
numerically performed in an efficient way using tools from optimal control.
These findings are illustrated on the examples of a rod with random elasticity
pulled by a time-dependent force, and the nonlinear Schr\"odinger equation
(NLSE) with random initial conditions
Optimal distributed control of a stochastic Cahn-Hilliard equation
We study an optimal distributed control problem associated to a stochastic
Cahn-Hilliard equation with a classical double-well potential and Wiener
multiplicative noise, where the control is represented by a source-term in the
definition of the chemical potential. By means of probabilistic and analytical
compactness arguments, existence of an optimal control is proved. Then the
linearized system and the corresponding backward adjoint system are analysed
through monotonicity and compactness arguments, and first-order necessary
conditions for optimality are proved.Comment: Key words and phrases: stochastic Cahn-Hilliard equation, phase
separation, optimal control, linearized state system, adjoint state system,
first-order optimality condition
Analysis of a splitting scheme for a class of nonlinear stochastic Schr\uf6dinger equations
We analyze the qualitative properties and the order of convergence of a splitting scheme for a class of nonlinear stochastic Schr\uf6dinger equations driven by additive It\uf4 noise. The class of nonlinearities of interest includes nonlocal interaction cubic nonlinearities. We show that the numerical solution is symplectic and preserves the expected mass for all times. On top of that, for the convergence analysis, some exponential moment bounds for the exact and numerical solutions are proved. This enables us to provide strong orders of convergence as well as orders of convergence in probability and almost surely. Finally, extensive numerical experiments illustrate the performance of the proposed numerical scheme
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