Results 11 to 20 of about 154,277 (267)
Stochastic chaplygin systems [PDF]
Reports on Mathematical Physics, 2010 We mimic the stochastic Hamiltonian reduction of Lazaro-Cami and Ortega [17, 18] for the case of certain non-holonomic systems with symmetries. Using the non-holonomic connection it is shown that the drift of the stochastically perturbed $n$-dimensional Chaplygin ball is a certain gradient of the density of the preserved measure of the deterministic ...
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Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory
Entropy, 2020 Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training ...
Sergey Oladyshkin +3 more
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Description of a stochastic system by a nonadapted stochastic process [PDF]
Physical Review E, 2021 11 pages 4 ...
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Stochastic Bridges of Linear Systems [PDF]
IEEE Transactions on Automatic Control, 2015 We study a generalization of the Brownian bridge as a stochastic process that models the position and velocity of inertial particles between the two end-points of a time interval. The particles experience random acceleration and are assumed to have known states at the boundary. Thus, the movement of the particles can be modeled as an Ornstein-Uhlenbeck
Yongxin Chen 0002, Tryphon T. Georgiou
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The decomposition of stochastic systems
Theoretical Computer Science, 1983 Using the deterministic framework of \textit{J. Hartmanis} and \textit{R. E. Stearns} [Algebraic structure theory of sequential machines (1966; Zbl 0154.417)] as a point of entry, the authors rely on use of an analogy to the substitution property to realize stochastic systems by the appropriate interconnection of smaller stochastic systems.
Taiho Kanaoka, Shingo Tomita
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On dissipation in stochastic systems [PDF]
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999 We define the property of dissipativity for controlled Ito diffusions. We investigate elementary properties, and we demonstrate that the framework is useful for control problems in which both probabilistic and worst-case representations of dynamic uncertainty are present. As an example we discuss a problem involving robust /spl Hscr//sub 2/ performance
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Optimization of stochastic systems [PDF]
Proceedings of the 18th conference on Winter simulation - WSC '86, 1986 This paper gives a short survey of Monte Carlo algorithms for stochastic optimization. Both discrete and continuous parameter stochastic optimization are discussed, with emphasis on the analysis of convergence rate. Some future research directions for the area are also indicated.
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Stochastic analysis of dimerization systems [PDF]
Physical Review E, 2009 The process of dimerization, in which two monomers bind to each other and form a dimer, is common in nature. This process can be modeled using rate equations, from which the average copy numbers of the reacting monomers and of the product dimers can then be obtained. However, the rate equations apply only when these copy numbers are large. In the limit
Barzel, Baruch, Biham, Ofer
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Fluctuation theorem for stochastic systems [PDF]
Physical Review E, 1999 Minor changes; typos corrected; accepted by Physical Review ...
Evans, Denis, Searles, Debra J
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Entropy, 2019 We show a link between Bayesian inference and information theory that is useful for model selection, assessment of information entropy and experimental design.
Sergey Oladyshkin, Wolfgang Nowak
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