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Статті в журналах з теми "Stochastique simulateur":
Noeiaghdam, Samad, Aliona Dreglea, Hüseyin Işık, and Muhammad Suleman. "A Comparative Study between Discrete Stochastic Arithmetic and Floating-Point Arithmetic to Validate the Results of Fractional Order Model of Malaria Infection." Mathematics 9, no. 12 (June 20, 2021): 1435. http://dx.doi.org/10.3390/math9121435.
Hachimi Alaoui, M. El-Hassan, and Imane Saad-Allah. "Effet de l’Ancrage des Anticipations d’Inflation Sous le Régime Intérimaire du Taux de Change au Maroc." European Scientific Journal, ESJ 19, no. 34 (December 31, 2023): 70. http://dx.doi.org/10.19044/esj.2023.v19n34p70.
Campillo, Fabien, Mohsen Chebbi, and Salwa Toumi. "Stochastic modeling for biotechnologies Anaerobic model AM2b." Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées Volume 28 - 2018 - 2019 -... (June 10, 2019). http://dx.doi.org/10.46298/arima.3159.
Дисертації з теми "Stochastique simulateur":
Zanolin, Anne. "Irrigation de précision en Petite Beauce : mesures au champ et modélisation stochastique spatialisée du fonctionnement hydrique et agronomique d'une parcelle de mai͏̈s." Paris 6, 2003. http://www.theses.fr/2003PA066344.
Basak, Subhasish. "Multipathogen quantitative risk assessment in raw milk soft cheese : monotone integration and Bayesian optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG021.
This manuscript focuses on Bayesian optimization of a quantitative microbiological risk assessment (QMRA) model, in the context of the European project ArtiSaneFood, supported by the PRIMA program. The primary goal is to establish efficient bio-intervention strategies for cheese producers in France.This work is divided into three broad directions: 1) development and implementation of a multipathogen QMRA model for raw milk soft cheese, 2) studying monotone integration methods for estimating outputs of the QMRA model, and 3) designing a Bayesian optimization algorithm tailored for a stochastic and computationally expensive simulator.In the first part we propose a multipathogen QMRA model, built upon existing studies in the literature (see, e.g., Bonifait et al., 2021, Perrin et al., 2014, Sanaa et al., 2004, Strickland et al., 2023). This model estimates the impact of foodborne illnesses on public health, caused by pathogenic STEC, Salmonella and Listeria monocytogenes, which can potentially be present in raw milk soft cheese. This farm-to-fork model also implements the intervention strategies related to mlik and cheese testing, which allows to estimate the cost of intervention. An implementation of the QMRA model for STEC is provided in R and in the FSKX framework (Basak et al., under review). The second part of this manuscript investigates the potential application of sequential integration methods, leveraging the monotonicity and boundedness properties of the simulator outputs. We conduct a comprehensive literature review on existing integration methods (see, e.g., Kiefer, 1957, Novak, 1992), and delve into the theoretical findings regarding their convergence. Our contribution includes proposing enhancements to these methods and discussion on the challenges associated with their application in the QMRA domain.In the final part of this manuscript, we propose a Bayesian multiobjective optimization algorithm for estimating the Pareto optimal inputs of a stochastic and computationally expensive simulator. The proposed approach is motivated by the principle of Stepwise Uncertainty Reduction (SUR) (see, e.g., Vazquezand Bect, 2009, Vazquez and Martinez, 2006, Villemonteix et al., 2007), with a weighted integrated mean squared error (w-IMSE) based sampling criterion, focused on the estimation of the Pareto front. A numerical benchmark is presented, comparing the proposed algorithm with PALS (Pareto Active Learning for Stochastic simulators) (Barracosa et al., 2021), over a set of bi-objective test problems. We also propose an extension (Basak et al., 2022a) of the PALS algorithm, tailored to the QMRA application case
Es-Sadek, Mohamed Zeriab. "Contribution à l'optimisation globale : approche déterministe et stochastique et application." Thesis, Rouen, INSA, 2009. http://www.theses.fr/2009ISAM0010/document.
This thesis concerns the global optimization of a non convex function under non linear restrictions, this problem cannot be solved using the classic deterministic methods like the projected gradient algorithm and the sqp method because they can solve only the convex problems. The stochastic algorithms like the genetic algorithm and the simulated annealing algorithm are also inefficients for solving this type of problems. For solving this kind of problems, we try to perturb stocasicly the deterministic classic method and to combine this perturbation with genetic algorithm and the simulated annealing. So we do the combination between the perturbed projected gradient and the genetic algorithm, the perturbed sqp method and the genetic algorithm, the perturbed projected gradient and the simulated annealing, the Piyavskii algorithm and the genetic algorithm. We applicate the coupled algorithms to different classic examples for concretited the thesis. For illustration in the real life, we applicate the coupled perturbed projected gradient end the genetic algorithm to logistic problem eventuelly transport. In this view, we sold the efficient practices
Pauwels, Benoît. "Optimisation sans dérivées sous incertitudes appliquées à des simulateurs coûteux." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30035/document.
The modeling of complex phenomena encountered in industrial issues can lead to the study of numerical simulation codes. These simulators may require extensive execution time (from hours to days), involve uncertain parameters and even be intrinsically stochastic. Importantly within the context of simulation-based optimization, the derivatives of the outputs with respect to the inputs may be inexistent, inaccessible or too costly to approximate reasonably. This thesis is organized in four chapters. The first chapter discusses the state of the art in derivative-free optimization and uncertainty modeling. The next three chapters introduce three independent---although connected---contributions to the field of derivative-free optimization in the presence of uncertainty. The second chapter addresses the emulation of costly stochastic simulation codes---stochastic in the sense simulations run with the same input parameters may lead to distinct outputs. Such was the matter of the CODESTOCH project carried out at the Summer mathematical research center on scientific computing and its applications (CEMRACS) during the summer of 2013, together with two Ph.D. students from Electricity of France (EDF) and the Atomic Energy and Alternative Energies Commission (CEA). We designed four methods to build emulators for functions whose values are probability density functions. These methods were tested on two toy functions and applied to industrial simulation codes concerned with three complex phenomena: the spatial distribution of molecules in a hydrocarbon system (IFPEN), the life cycle of large electric transformers (EDF) and the repercussions of a hypothetical accidental in a nuclear plant (CEA). Emulation was a preliminary process towards optimization in the first two cases. In the third chapter we consider the influence of inaccurate objective function evaluations on direct search---a classical derivative-free optimization method. In real settings inaccuracy may never vanish, however users usually apply direct search algorithms disregarding inaccuracy. We raise three questions. What precision can we hope to achieve, given the inaccuracy? How fast can this precision be attained? What stopping criteria can guarantee this precision? We answer these three questions for directional direct search applied to objective functions whose evaluation inaccuracy stochastic or not is uniformly bounded. We also derive from our results an adaptive algorithm for dealing efficiently with several oracles having different levels of accuracy. The theory and algorithm are validated with numerical tests and two industrial applications: surface minimization in mechanical design and oil well placement in reservoir engineering. The fourth chapter considers optimization problems with imprecise parameters, whose imprecision is modeled with fuzzy sets theory. A number of methods have been published to solve linear programs involving fuzzy parameters, but only a few as for nonlinear programs. We propose an algorithm to address a large class of fuzzy optimization problems by iterative non-dominated sorting. The distributions of the fuzzy parameters are assumed only partially known. We also provide a criterion to assess the precision of the solutions and make comparisons with other methods found in the literature. We show that our algorithm guarantees solutions whose level of precision at least equals the precision on the available data
Bouallagui, Sarra. "Techniques d'optimisation déterministe et stochastique pour la résolution de problèmes difficiles en cryptologie." Phd thesis, INSA de Rouen, 2010. http://tel.archives-ouvertes.fr/tel-00557912.
Pan-Yu, Yiyan. "Spectres de processus de Markov." Phd thesis, Université Joseph Fourier (Grenoble), 1997. http://tel.archives-ouvertes.fr/tel-00004959.
Ben, Salah Riadh. "Élaboration d'une méthode tomographique de reconstruction 3D en vélocimétrie par image de particules basée sur les processus ponctuels marqués." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2268/document.
The research work fulfilled in this thesis fit within the development of optical measurement techniques for fluid mechanics. They are particularly related to 3D particle volume reconstruction in order to infer their movement. This volumetric measurement technic, called Tomo-PIV has appeared on 2006 and has been the subject of several works to enhance the reconstruction, which represents one of the most important steps of this measurement technique. The proposed methods in Literature don't necessarily take into account the particular form of objects to reconstruct and they are not sufficiently robust to deal with noisy images. To deal with these challenges, we propose a tomographic reconstruction method, called (IOD-PVRMPP), and based on marked point processes. Our method allows solving the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem, which is inherent to voxel-based approaches. The reconstruction of a 3D particle set is obtained by minimizing an energy function, which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method, which provides the initial distribution of 3D particles based on the detection of 2D particles located in projection images. Finally, this method is applied to simulated fluid flow or real one produced in an open channel flow behind a turbulent grid. The results and the comparisons of this method with classical ones show the great interest of this parsimonious approach
Книги з теми "Stochastique simulateur":
Canada. Ministère de l'environnement. Institut national de recherche en hydrologie. Simulateur numérique de l'écoulement et du transfert de masse dans des réseaux stochastiques de fractures. S.l: s.n, 1988.
Тези доповідей конференцій з теми "Stochastique simulateur":
Aupetit, B., M. Batteux, A. Rauzy, and J. M. Roussel. "Vers la définition d'un kit d'évaluation pour les simulateurs stochastiques." In Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, 11-13 Octobre 2016, Saint Malo, France. IMdR, 2016. http://dx.doi.org/10.4267/2042/61811.