Literatura académica sobre el tema "Stochastic inverse modeling"
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Artículos de revistas sobre el tema "Stochastic inverse modeling"
Hestir, Kevin, Stephen J. Martel, Stacy Vail, Jane Long, Pete D'Onfro y William D. Rizer. "Inverse hydrologic modeling using stochastic growth algorithms". Water Resources Research 34, n.º 12 (diciembre de 1998): 3335–47. http://dx.doi.org/10.1029/98wr01549.
Texto completoHohenegger, Christel y M. Gregory Forest. "Direct and inverse modeling for stochastic passive microbead rheology". PAMM 7, n.º 1 (diciembre de 2007): 1110505–6. http://dx.doi.org/10.1002/pamm.200700640.
Texto completoPonski, Mariusz y Andrzej Sluzalec. "Modeling and Simulation of Stochastic Inverse Problems in Viscoplasticity". Transactions of the Indian Institute of Metals 72, n.º 10 (27 de junio de 2019): 2803–17. http://dx.doi.org/10.1007/s12666-019-01757-2.
Texto completoBoluh, Kateryna y Natalija Shchestyuk. "Simulating Stochastic Diffusion Processes and Processes with “Market” Time". Mohyla Mathematical Journal 3 (29 de enero de 2021): 25–30. http://dx.doi.org/10.18523/2617-70803202025-30.
Texto completoPreziosi, L., G. Teppati y N. Bellomo. "Modeling and solution of stochastic inverse problems in mathematical physics". Mathematical and Computer Modelling 16, n.º 5 (mayo de 1992): 37–51. http://dx.doi.org/10.1016/0895-7177(92)90118-5.
Texto completoAbou-Elyazied Abdallh, Ahmed y Luc Dupré. "Stochastic modeling error reduction using Bayesian approach coupled with an adaptive Kriging-based model". COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, n.º 3 (29 de abril de 2014): 856–67. http://dx.doi.org/10.1108/compel-10-2012-0230.
Texto completoLlopis-Albert, Carlos, Francisco Rubio y Francisco Valero. "Characterization and assessment of composite materials via inverse finite element modeling". Multidisciplinary Journal for Education, Social and Technological Sciences 6, n.º 2 (3 de octubre de 2019): 1. http://dx.doi.org/10.4995/muse.2019.12374.
Texto completoHan, S. L. y Takeshi Kinoshita. "Stochastic inverse modeling of nonlinear roll damping moment of a ship". Applied Ocean Research 39 (enero de 2013): 11–19. http://dx.doi.org/10.1016/j.apor.2012.09.003.
Texto completoLlopis-Albert, Carlos, Francisco Rubio, Francisco Valero, Hunchang Liao y Shouzhen Zeng. "Stochastic inverse finite element modeling for characterization of heterogeneous material properties". Materials Research Express 6, n.º 11 (23 de octubre de 2019): 115806. http://dx.doi.org/10.1088/2053-1591/ab4c72.
Texto completoLi, Liangping, Sanjay Srinivasan, Haiyan Zhou y J. Jaime Gómez-Hernández. "A local–global pattern matching method for subsurface stochastic inverse modeling". Environmental Modelling & Software 70 (agosto de 2015): 55–64. http://dx.doi.org/10.1016/j.envsoft.2015.04.008.
Texto completoTesis sobre el tema "Stochastic inverse modeling"
Zhou, Haiyan. "Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers". Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/12267.
Texto completoZhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267
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Fu, Jianlin. "A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment". Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1969.
Texto completoFu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1969
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Mao, Deqiang. "Stochastic Analysis of Pumping Tests in Unconfined Aquifers". Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222873.
Texto completoBuchin, Anatoly. "Modeling of single cell and network phenomena of the nervous system : ion dynamics during epileptic oscillations and inverse stochastic resonance". Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0041/document.
Texto completoIn this thesis we used dynamical systems methods and numericalsimulations to study the mechanisms of epileptic oscillations associated with ionconcentration changes and cerebellar Purkinje cell bimodal behavior. The general issue in this work is the interplay between single neuron intrinsicproperties and synaptic input structure controlling the neuronal excitability. In the first part of this thesis we focused on the role of the cellular intrinsicproperties, their control over the cellular excitability and their response to thesynaptic inputs. Specifically we asked the question how the cellular changes ininhibitory synaptic function might lead to the pathological neural activity. We developed a model of seizure initiation in temporal lobe epilepsy. Specifically we focused on the role of KCC2 cotransporter that is responsible for maintaining the baseline extracellular potassium and intracellular chloride levels in neurons. Recent experimental data has shown that this cotransporter is absent in the significant group of pyramidal cells in epileptic patients suggesting its epileptogenic role. We found that addition of the critical amount of KCC2-deficient pyramidal cells to the realistic subiculum network can switch the neural activity from normal to epileptic oscillations qualitatively reproducing the activity recorded in human epileptogenic brain slices. In the second part of this thesis we studied how synaptic noise might control the Purkinje cell excitability. We investigated the effect of spike inhibition caused by noise current injection, so-called inverse stochastic resonance (ISR). This effect has been previously found in single neuron models while we provided its first experimental evidence. We found that Purkinje cells in brain slices could be efficiently inhibited by current noise injections. This effect is well reproduced by the phenomenological model fitted for different cells. Using methods of information theory we showed that ISR supports an efficient information transmission of single Purkinje cells suggesting its role for cerebellar computations
Dedes, Nonell Irene. "Stochastic approach to the problem of predictive power in the theoretical modeling of the mean-field". Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAE017/document.
Texto completoResults of our study of the theoretical modelling capacities focussing on the nuclear phenomenological mean-field approaches are presented. It is expected that a realistic theory should be capable of predicting satisfactorily the results of the experiments to come, i.e., having what is called a good predictive power. To study the predictive power of a theoretical model, we had to take into account not only the errors of the experimental data but also the uncertainties originating from approximations of the theoretical formalism and the existence of parametric correlations. One of the central techniques in the parameter adjustment is the solution of what is called the Inverse Problem. Parametric correlations usually induce ill-posedness of the inverse problem; they need to be studied and the model regularised. We have tested two types of realistic phenomenological Hamiltonians showing how to eliminate the parametric correlations theoretically and in practice. We calculate the level confidence intervals, the uncertainty distributions of model predictions and have shown how to improve theory’s prediction capacities and stability
Zeren, Zafer. "Lagrangian stochastic modeling of turbulent gas-solid flows with two-way coupling in homogeneous isotropic turbulence". Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0106/document.
Texto completoIn this thesis, performed in IMFT, we are interested in the turbulent gas-solid flows and more specifically, in the phenomenon of turbulence modulation which is the modification of the structure of the turbulence due to the solid particles. This mechanism is crucial in flows with high particle mass-loadings. In this work, we considered a homogeneous isotropic turbulence without gravity kept stationary with stochastic type forcing. Discrete particles are tracked individually in Lagrangian manner. Turbulence of the carrier phase is obtained by using DNS. The particles are spherical, rigid and of a diameter smaller than the smallest scales of turbulence. Their density is very large in comparison to the density of the fluid. In this configuration the only force acting on the particles is the drag force. Volume fraction of particles is very small and inter-particle interactions are not considered. To model this type of flow, a stochastic approach is used where the fluid element accel- eration is modeled using stochastic Langevin equation. The originality in this work is an additional term in the stochastic equation which integrates the effect of the particles on the trajectory of fluid elements. To model this term, we proposed two types of modeling: a mean drag model which is defined using the mean velocities from the mean transport equations of the both phases and an instantaneous drag term which is written with the help of the Mesoscopic Eulerian Approach. The closure of the models is based on the Lagrangian auto- correlation function of the fluid velocity and on the transport equation of the fluid kinetic energies. The models are tested in terms of the fluid-particle correlations and fluid-particle turbulent drift velocity. The results show that the mean model, simple, takes into account the principal physical mechanism of turbulence modulation. However, practical closure problem is brought forward to the Lagrangian integral scale and the fluid kinetic energy of the fluid turbulence viewed by the particles
Nguyen, Manh Tu. "Identification multi-échelle du champ d'élasticité apparent stochastique de microstructures hétérogènes : application à un tissu biologique". Thesis, Paris Est, 2013. http://www.theses.fr/2013PEST1135/document.
Texto completoIn the framework of linear elasticity 3D for complex microstructures that cannot be simply described in terms of components such as biological tissues, we propose, in this research work, a methodology for multiscale experimental identification of the apparent elasticity random field of the microstructure at mesoscopic scale using displacement field measurements at macroscopic scale and mesoscopic scale. We can then use this methodology in the case of changing scale to obtain the mechanical properties at macroscale. In this context, the major issue is the experimental identification by solving a statistical inverse problem of the stochastic modeling introduced for the apparent elasticity random field at mesoscale. This experimental identification allows to validate the modeling and makes it useful for existing materials with complex microstructures. This research work is proposed in this context in which experimentation and experimental validation based on simultaneous measurements of field imaging at macroscale and mesoscale are made on the cortical bonemakes it useful for existing materials with complex microstructures. This research work is proposed in this context in which experimentation and experimental validation based on simultaneous measurements of field imaging at macroscale and mesoscale are made on the cortical bone
Lestoille, Nicolas. "Stochastic model of high-speed train dynamics for the prediction of long-time evolution of the track irregularities". Thesis, Paris Est, 2015. http://www.theses.fr/2015PEST1094/document.
Texto completoRailways tracks are subjected to more and more constraints, because the number of high-speed trains using the high-speed lines, the trains speed, and the trains load keep increasing. These solicitations contribute to produce track irregularities. In return, track irregularities influence the train dynamic responses, inducing degradation of the comfort. To guarantee good conditions of comfort in the train, railways companies perform maintenance operations of the track, which are very costly. Consequently, there is a great interest for the railways companies to predict the long-time evolution of the track irregularities for a given track portion, in order to be able to anticipate the start off of the maintenance operations, and therefore to reduce the maintenance costs and to improve the running conditions. In this thesis, the long-time evolution of a given track portion is analyzed through a vector-valued indicator on the train dynamics. For this given track portion, a local stochastic model of the track irregularities is constructed using a global stochastic model of the track irregularities and using big data made up of experimental measurements of the track irregularities performed by a measuring train. This local stochastic model takes into account the variability of the track irregularities and allows for generating realizations of the track irregularities at each long time. After validating the computational model of the train dynamics, the train dynamic responses on the measured track portion are numerically simulated using the local stochastic model of the track irregularities. A vector-valued random dynamic indicator is defined to characterize the train dynamic responses on the given track portion. This dynamic indicator is constructed such that it takes into account the model uncertainties in the train dynamics computational model. For the identification of the track irregularities stochastic model and the characterization of the model uncertainties, advanced stochastic methods such as the polynomial chaos expansion and the multivariate maximum likelihood are applied to non-Gaussian and non-stationary random fields. Finally, a stochastic predictive model is proposed for predicting the statistical quantities of the random dynamic indicator, which allows for anticipating the need for track maintenance. This modeling is constructed using the results of the train dynamics simulation and consists in using a non-stationary Kalman-filter type model with a non-Gaussian initial condition. The proposed model is validated using experimental data for the French railways network for the high-speed trains
Zhang, Tianyu. "Problème inverse statistique multi-échelle pour l'identification des champs aléatoires de propriétés élastiques". Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2068.
Texto completoWithin the framework of linear elasticity theory, the numerical modeling and simulation of the mechanical behavior of heterogeneous materials with complex random microstructure give rise to many scientific challenges at different scales. Despite that at macroscale such materials are usually modeled as homogeneous and deterministic elastic media, they are not only heterogeneous and random at microscale, but they often also cannot be properly described by the local morphological and mechanical properties of their constituents. Consequently, a mesoscale is introduced between macroscale and microscale, for which the mechanical properties of such a random linear elastic medium are represented by a prior non-Gaussian stochastic model parameterized by a small or moderate number of unknown hyperparameters. In order to identify these hyperparameters, an innovative methodology has been recently proposed by solving a multiscale statistical inverse problem using only partial and limited experimental data at both macroscale and mesoscale. It has been formulated as a multi-objective optimization problem which consists in minimizing a (vector-valued) multi-objective cost function defined by three numerical indicators corresponding to (scalar-valued) single-objective cost functions for quantifying and minimizing distances between multiscale experimental data measured simultaneously at both macroscale and mesoscale on a single specimen subjected to a static test, and the numerical solutions of deterministic and stochastic computational models used for simulating the multiscale experimental test configuration under uncertainties. This research work aims at contributing to the improvement of the multiscale statistical inverse identification method in terms of computational efficiency, accuracy and robustness by introducing (i) an additional mesoscopic numerical indicator allowing the distance between the spatial correlation length(s) of the measured experimental fields and the one(s) of the computed numerical fields to be quantified at mesoscale, so that each hyperparameter of the prior stochastic model has its own dedicated single-objective cost-function, thus allowing the time-consuming global optimization algorithm (genetic algorithm) to be avoided and replaced with a more efficient algorithm, such as the fixed-point iterative algorithm, for solving the underlying multi-objective optimization problem with a lower computational cost, and (ii) an ad hoc stochastic representation of the hyperparameters involved in the prior stochastic model of the random elasticity field at mesoscale by modeling them as random variables, for which the probability distributions can be constructed by using the maximum entropy principle under a set of constraints defined by the available and objective information, and whose hyperparameters can be determined using the maximum likelihood estimation method with the available data, in order to enhance both the robustness and accuracy of the statistical inverse identification method of the prior stochastic model. Meanwhile, we propose as well to solve the multi-objective optimization problem by using machine learning based on artificial neural networks. Finally, the improved methodology is first validated on a fictitious virtual material within the framework of 2D plane stress and 3D linear elasticity theory, and then illustrated on a real heterogenous biological material (beef cortical bone) in 2D plane stress linear elasticity
Robin, Frédérique. "Modeling and analysis of cell population dynamics : application to the early development of ovarian follicles". Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS344.
Texto completoThis thesis aims to design and analyze population dynamics models dedicated to the dynamics of somatic cells during the early stages of ovarian follicle growth. The model behaviors are analyzed through theoretical and numerical approaches, and the calibration of parameters is performed by proposing maximum likelihood strategies adapted to our specific dataset. A non-linear stochastic model, that accounts for the joint dynamics of two cell types (precursors and proliferative), is dedicated to the activation of follicular growth. In particular, we compute the extinction time of precursor cells. A rigorous finite state projection approach is implemented to characterize the system state at extinction. A linear multitype age-structured model for the proliferative cell population is dedicated to the early follicle growth. The different types correspond here to the spatial cell positions. This model is of decomposable kind; the transitions are unidirectional from the first to the last spatial type. We prove the long-term convergence for both the stochastic Bellman-Harris model and the multi-type McKendrick-VonFoerster equation. We adapt existing results in a context where the Perron-Frobenius theorem does not apply, and obtain explicit analytical formulas for the asymptotic moments of cell numbers and stable age distribution. We also study the well-posedness of the inverse problem associated with the deterministic model
Libros sobre el tema "Stochastic inverse modeling"
Ravalec, Mickaële Le. Inverse stochastic modeling of flow in porous media: Applications to reservoir characterization. Paris: Editions Technip, 2005.
Buscar texto completoInverse Stochastic Modeling of Flow in Porous Media: Application to Reservoir Characterization (Ifp Publications). Technip, 2005.
Buscar texto completoCapítulos de libros sobre el tema "Stochastic inverse modeling"
Kitanidis, Peter K. "On stochastic inverse modeling". En Subsurface Hydrology: Data Integration for Properties and Processes, 19–30. Washington, D. C.: American Geophysical Union, 2007. http://dx.doi.org/10.1029/171gm04.
Texto completoSun, Ne-Zheng. "The Stochastic Method for Solving Inverse Problems". En Inverse Problems in Groundwater Modeling, 141–93. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-1970-4_7.
Texto completoGómez-Hernández, J. Jaime y Jianlin Fu. "Blocking Markov Chain Monte Carlo Schemes for Inverse Stochastic Hydrogeological Modeling". En Quantitative Geology and Geostatistics, 121–26. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-2322-3_11.
Texto completoWilson, J. D., T. K. Flesch y B. P. Crenna. "Estimating Surface-Air Gas Fluxes by Inverse Dispersion Using a Backward Lagrangian Stochastic Trajectory Model". En Lagrangian Modeling of the Atmosphere, 149–62. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/2012gm001269.
Texto completoFu, Jianlin, J. Jaime Gómez-Hernández y Song Du. "A Gradient-Based Blocking Markov Chain Monte Carlo Method for Stochastic Inverse Modeling". En Geostatistics Valencia 2016, 777–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46819-8_53.
Texto completoBuldygin, Valeriĭ V., Karl-Heinz Indlekofer, Oleg I. Klesov y Josef G. Steinebach. "Asymptotically Quasi-inverse Functions". En Probability Theory and Stochastic Modelling, 229–310. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99537-3_7.
Texto completoKiselev, O. M. "Stochastic Properties of an Inverted Pendulum on a Wheel on a Soft Surface". En 13th Chaotic Modeling and Simulation International Conference, 361–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70795-8_28.
Texto completoLobo-Ferreira, J. P. "On the Application of Stochastic Inverse Modelling to the Fractured Semi-Confined Aquifer of Bagueixe, Portugal". En geoENV I — Geostatistics for Environmental Applications, 39–50. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1675-8_4.
Texto completoFernández-Martínez, Juan Luis, Esperanza García-Gonzalo, Saras Saraswathi, Robert Jernigan y Andrzej Kloczkowski. "Particle Swarm Optimization: A Powerful Family of Stochastic Optimizers. Analysis, Design and Application to Inverse Modelling". En Lecture Notes in Computer Science, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21515-5_1.
Texto completoLobo-Ferreira, J. P. "Applicability of Stochastic Inverse Modelling, Aquifer Vulnerability Assessment, Groundwater Flow and Mass Transport Modelling in the Fractured Semi Confined Aquifer of Bagueixe, in Portugal". En Regional Approaches to Water Pollution in the Environment, 251–81. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0345-6_12.
Texto completoActas de conferencias sobre el tema "Stochastic inverse modeling"
Jones, N. L., J. I. Green y J. R. Walker. "Stochastic Inverse Modeling for Capture Zone Analysis". En Probabilistic Approaches to Groundwater Modeling Symposium at World Environmental and Water Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40696(2003)1.
Texto completoSerino, Gennaro, T. Magin, Patrick Rambaud y Fabio Pinna. "Statistical inverse analysis and stochastic modeling of transition". En 43rd AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2883.
Texto completoKaulakys, Bronislovas y Miglius Alaburda. "Modeling the inverse cubic distributions by nonlinear stochastic differential equations". En 2011 21st International Conference on Noise and Fluctuations (ICNF). IEEE, 2011. http://dx.doi.org/10.1109/icnf.2011.5994380.
Texto completoJorge, Ariosto, Patricia Lopes y Sebastião Cunha. "Modeling of an Inverse Problem for Damage Detection Using Stochastic Optimization Techniques". En 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5837.
Texto completoPace, F., A. Santilano, A. Godio y A. Manzella. "Stochastic Inverse Modeling of Magnetotelluric Data from the Larderello-Travale Geothermal Area (Italy)". En 1st Conference on Geophysics for Geothermal-Energy Utilization and Renewable-Energy Storage. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902507.
Texto completoGuadagnini, A., A. Russian, M. Riva, E. R. Russo y M. A. Chiaramonte. "Quantification of Uncertainties of Fracture Permeability Via Mud Loss Information and Inverse Stochastic Modeling". En 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901624.
Texto completoMa, Rui y John B. Ferris. "Terrain Gridding Using a Stochastic Weighting Function". En ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6085.
Texto completoMarkov, Pavel. "New Technology for Inverse Problem Solving of Digital Core Model Construction Using Stochastic Modeling and Particle Swarm Optimization". En SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201944-ms.
Texto completoMarkov, Pavel. "New Technology for Inverse Problem Solving of Digital Core Model Construction Using Stochastic Modeling and Particle Swarm Optimization (Russian)". En SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201944-ru.
Texto completoSalama, Mostafa y Vladimir V. Vantsevich. "Mechatronics Implementation of Inverse Dynamics-Based Controller for an Off-Road UGV". En ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51010.
Texto completoInformes sobre el tema "Stochastic inverse modeling"
GÓMEZ-HERNÁNDEZ, J. Jaime, Haiyan ZHOU, Liangping LI y Harrie-Jan HENDRICKS FRANSSEN. Abnormal Inverse Stochastic Modeling. Cogeo@oeaw-giscience, septiembre de 2011. http://dx.doi.org/10.5242/iamg.2011.0131.
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