A crucial aspect in system identification is the assessment of accuracy identified matrices. Stochastic Subspace Identification (SSI) a widely used approach for linear systems from output-only data because it combines high computational robustness and efficiency with estimation accuracy. Practical approaches estimating (co)variance matrices that are using SSI exist case where obtained shift-inv...

In this paper, we study a class of optimal stochastic control problems involving two different time scales. The fast mode of the system is represented by deterministic state equations whereas the slow mode of the system corresponds to a jump disturbance process. Under a fundamental “ergodicity” property for a class of “infinitesimal control systems” associated with the fast mode, we show that t...

There is great uncertainty about future climate conditions and the appropriate policies for managing interactions between the climate and the economy. We develop a multidimensional computational model to examine how uncertainties and risks in the economic and climate systems affect the social cost of carbon (SCC)—that is, the present value of the marginal damage to economic output caused by car...

We study the controllability properties of the class of stochastic differential systems characterized by a linear controlled diffusion perturbed by a smooth, bounded, uniformly Lipschitz nonlinearity. We obtain conditions that guarantee the weak and strong controllability of the system. Also, given any open set in the state space we construct a control, depending only on the Lipschitz constant ...

Computational uncertainty quantification in a probabilistic setting is a special case of a parametric problem. Parameter dependent state vectors lead via association to a linear operator to analogues of covariance, its spectral decomposition, and the associated Karhunen-Loève expansion. From this one obtains a generalised tensor representation. The parameter in question may be a tuple of number...

Uncertainty quantification has become an important task and an emerging topic in many engineering fields. Uncertainties can be caused by many factors, including inaccurate component models, the stochastic nature of some design parameters, external environmental fluctuations (e.g., temperature variation), measurement noise, and so forth. In order to enable robust engineering design and optimal d...

Scientific computations for the quantification, estimation and prediction of uncertainties for ocean dynamics are developed and exemplified. Primary characteristics of ocean data, models and uncertainties are reviewed and quantitative data assimilation concepts defined. Challenges involved in realistic data-driven simulations of uncertainties for four-dimensional interdisciplinary ocean process...

R , U ) and (Uk ,Uk) ) , k = 1,2 , . . . , N , are measurable spaces and J k , k = 1,2 , . . . , N , are subfields of the product field 13@(@, U i ) . Consider an N-tuple of functions y := ( y l , y2 , . . . , y N ) for which yk , k = 1,2 , . . . , N , is J k / U k measurable. If for each w E 0 there exists a unique u := ( u l , u2 , . . . , u N ) E nLl U i satisfying the equations u k = yk(w, ...