Modelling and solution methods for stochastic optimisation [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this thesis we consider two research problems, namely, (i) language constructs for modelling stochastic programming (SP) problems and (ii) solution ...
Zverovich, Victor
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A comparative study of immersed-boundary interpolation methods for a flow around a stationary cylinder at low Reynolds number [PDF]
, 2013 The accuracy and computational efficiency of various interpolation methods for the implementation of non grid-confirming boundaries is assessed. The aim of the research is to select an interpolation method that is both efficient and sufficiently accurate
Wissink, J, Bahai, H, Madani, SH
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Optimal control of stochastic partial differential equations in Banach spaces [PDF]
, 2010 In this thesis we study optimal control problems in Banach spaces for stochastic partial differential equations. We investigate two different approaches.
Serrano Perdomo, Rafael Antonio
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Noise-Immune Machine Learning and Autonomous Grid Control
IEEE Open Access Journal of Power and Energy, 2023 Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control.
James Obert +2 more
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Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022 Many complex real world phenomena exhibit abrupt, intermittent, or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian Lévy noise. Among these complex phenomena, the most likely transition paths between metastable states are important since these rare events may have a high impact in ...
Wei Wei +3 more
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Multi-Objective LQG Design with Primal-Dual Method
Mathematics, 2023 The objective of this paper is to investigate a multi-objective linear quadratic Gaussian (LQG) control problem. Specifically, we examine an optimal control problem that minimizes a quadratic cost over a finite time horizon for linear stochastic systems ...
Donghwan Lee
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Modelling the evolution of transcriptional control networks using stochastic simulations and evolutionary computational methods [PDF]
BMC Systems Biology, 2007 Organisms live in a constantly varying environment with limited resources. In order to thrive, organisms need to be able to respond to environmental changes, to make best use of the available resources, or to protect themselves from potentially harmful agents.
Dafyd J. Jenkins, Dov J. Stekel
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Chebyshev wavelet-based method for solving various stochastic optimal control problems and its application in finance [PDF]
Iranian Journal of Numerical Analysis and OptimizationIn this paper, a computational method based on parameterizing state and control variables is presented for solving Stochastic Optimal Control (SOC) problems.
M. Yarahmadi, S. Yaghobipour
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Using Stochastic Approximation Methods to Compute Optimal Base-Stock Levels in Inventory Control Problems [PDF]
Operations Research, 2008 In this paper, we consider numerous inventory control problems for which the base-stock policies are known to be optimal, and we propose stochastic approximation methods to compute the optimal base-stock levels. The existing stochastic approximation methods in the literature guarantee that their iterates converge, but not necessarily to the optimal ...
Sumit Kunnumkal, Huseyin Topaloglu
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Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. [PDF]
, 2008 We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as
Fearnhead, Paul, Paul Fearnhead
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