Relevant bibliographies by topics / Stochastic inverse modeling

Academic literature on the topic 'Stochastic inverse modeling'

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Published: 14 March 2023

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Journal articles on the topic "Stochastic inverse modeling"

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Hestir, Kevin, Stephen J. Martel, Stacy Vail, Jane Long, Pete D'Onfro, and William D. Rizer. "Inverse hydrologic modeling using stochastic growth algorithms." Water Resources Research 34, no. 12 (December 1998): 3335–47. http://dx.doi.org/10.1029/98wr01549.

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Hohenegger, Christel, and M. Gregory Forest. "Direct and inverse modeling for stochastic passive microbead rheology." PAMM 7, no. 1 (December 2007): 1110505–6. http://dx.doi.org/10.1002/pamm.200700640.

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Ponski, Mariusz, and Andrzej Sluzalec. "Modeling and Simulation of Stochastic Inverse Problems in Viscoplasticity." Transactions of the Indian Institute of Metals 72, no. 10 (June 27, 2019): 2803–17. http://dx.doi.org/10.1007/s12666-019-01757-2.

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Boluh, Kateryna, and Natalija Shchestyuk. "Simulating Stochastic Diffusion Processes and Processes with “Market” Time." Mohyla Mathematical Journal 3 (January 29, 2021): 25–30. http://dx.doi.org/10.18523/2617-70803202025-30.

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The paper focuses on modelling, simulation techniques and numerical methods concerned stochastic processes in subject such as financial mathematics and financial engineering. The main result of this work is simulation of a stochastic process with new market active time using Monte Carlo techniques.The processes with market time is a new vision of how stock price behavior can be modeled so that the nature of the process is more real. The iterative scheme for computer modelling of this process was proposed.It includes the modeling of diffusion processes with a given marginal inverse gamma distribution. Graphs of simulation of the Ornstein-Uhlenbeck random walk for different parameters, a simulation of the diffusion process with a gamma-inverse distribution and simulation of the process with market active time are presented.To simulate stochastic processes, an iterative scheme was used: xk+1 = xk + a(xk, tk) ∆t + b(xk, tk) √ (∆t) εk,, where εk each time a new generation with a normal random number distribution.Next, the tools of programming languages for generating random numbers (evenly distributed, normally distributed) are investigated. Simulation (simulation) of stochastic diffusion processes is carried out; calculation errors and acceleration of convergence are calculated, Euler and Milstein schemes. At the next stage, diffusion processes with a given distribution function, namely with an inverse gamma distribution, were modelled. The final stage was the modelling of stock prices with a new "market" time, the growth of which is a diffusion process with inverse gamma distribution. In the proposed iterative scheme of stock prices, we use the modelling of market time gains as diffusion processes with a given marginal gamma-inverse distribution.The errors of calculations are evaluated using the Milstein scheme. The programmed model can be used to predict future values of time series and for option pricing.
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Preziosi, L., G. Teppati, and N. Bellomo. "Modeling and solution of stochastic inverse problems in mathematical physics." Mathematical and Computer Modelling 16, no. 5 (May 1992): 37–51. http://dx.doi.org/10.1016/0895-7177(92)90118-5.

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Abou-Elyazied Abdallh, Ahmed, and 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, no. 3 (April 29, 2014): 856–67. http://dx.doi.org/10.1108/compel-10-2012-0230.

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Purpose – Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution. Design/methodology/approach – The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique. Findings – The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage. Originality/value – The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.
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Llopis-Albert, Carlos, Francisco Rubio, and Francisco Valero. "Characterization and assessment of composite materials via inverse finite element modeling." Multidisciplinary Journal for Education, Social and Technological Sciences 6, no. 2 (October 3, 2019): 1. http://dx.doi.org/10.4995/muse.2019.12374.

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<p class="Textoindependiente21">Characterizing mechanical properties play a major role in several fields such as biomedical and manufacturing sectors. In this study, a stochastic inverse model is combined with a finite element (FE) approach to infer full-field mechanical properties from scarce experimental data. This is achieved by means of non-linear combinations of material property realizations, with a certain spatial structure, for constraining stochastic simulations to data within a non-multiGaussian framework. This approach can be applied to the design of highly heterogenous materials, the uncertainty assessment of unknown mechanical properties or to provide accurate medical diagnosis of hard and soft tissues. The developed methodology has been successfully applied to a complex case study.</p>
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Han, S. L., and Takeshi Kinoshita. "Stochastic inverse modeling of nonlinear roll damping moment of a ship." Applied Ocean Research 39 (January 2013): 11–19. http://dx.doi.org/10.1016/j.apor.2012.09.003.

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Llopis-Albert, Carlos, Francisco Rubio, Francisco Valero, Hunchang Liao, and Shouzhen Zeng. "Stochastic inverse finite element modeling for characterization of heterogeneous material properties." Materials Research Express 6, no. 11 (October 23, 2019): 115806. http://dx.doi.org/10.1088/2053-1591/ab4c72.

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Li, Liangping, Sanjay Srinivasan, Haiyan Zhou, and J. Jaime Gómez-Hernández. "A local–global pattern matching method for subsurface stochastic inverse modeling." Environmental Modelling & Software 70 (August 2015): 55–64. http://dx.doi.org/10.1016/j.envsoft.2015.04.008.

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Dissertations / Theses on the topic "Stochastic inverse modeling"

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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.

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La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana.
Zhou ., 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.

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Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics. First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk particle-tracking method, which enhances the com
Fu, 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.

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The S shaped log log drawdown time curve typical of pumping tests in unconfined aquifers is reinvestigated via numerical experiments. We examine the temporal and spatial evolution of the rate of change in storage in an unconfined aquifer during pumping. This evolution is related to the transition of water release mechanisms from the expansion of water and compaction of the porous medium to the drainage of water from the unsaturated zone above the initial water table and initially saturated pores as the water table falls during the pumping of the aquifer. We conclude that the transition of the water release mechanisms and vertical flow are the cause of the S shaped drawdown time. Cross-correlation analysis is then employed to examine the relationship between hydraulic properties of an unconfined aquifer and pressure observations. The analysis reveals that head observed in the saturated zone at late times along a streamline is positively correlated with the conductivity (K(s)) of the region upstream of the observation location, and negatively correlated with the K(s) of the region downstream of the observation location along the same streamline. Besides, head observations in the saturated zone at the early time are positively correlated with specific storage (S(s)) in a narrow region between the observation and pumping locations. At intermediate and late times, the head positively correlates with the heterogeneity of α (pore-size distribution parameter) in a thin disk-shaped unsaturated region above the pumping and observation locations. Saturated water content θ(s) in the vadose zone directly above the pumping and monitoring locations is found positively correlated with the head observations during the intermediate times and late times.In the end, a stochastic inverse estimation is conducted to jointly interpret a sequential pumping test in a three dimensional unconfined aquifer. K(s), S(s), θ(s) and α are estimated at the same time. The estimated results capture the pattern of the heterogeneous parameters as well as the details with a smooth distribution. The estimated heterogeneous parameter fields produce better head predictions than the traditional homogeneous method.
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Buchin, 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.

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Dans cette thèse nous avons utilisé des méthodes de systèmes dynamiques et des simulations numériques pour étudier les mécanismes d'oscillations d'épilepsie associés à des concentrations d’ions dynamiques et au comportement bimodal des cellules Purkinje du cervelet. Le propos général de ce travail est l'interaction entre les propriétés intrinsèques des neurones simple et la structure d'entrée synaptique contrôlant l'excitabilité neuronale. Dans la première partie de la thèse nous avons développé un modèle de transition de crise épileptique dans le lobe temporal du cerveau. Plus précisément nous nous sommes concentrés sur le rôle du cotransporteur KCC2, qui est responsable de la maintenance du potassium extracellulaire et du chlorure intracellulaire dans les neurones. Des données expérimentales récentes ont montré que cette molécule est absente dans un groupe significatif de cellules pyramidales dans le tissue neuronal de patients épileptiques suggérant son rôle épileptogène. Nous avons trouvé que l'addition d’une quantité critique de cellules pyramidale KCC2 déficient au réseau de subiculum, avec une connectivité réaliste, peut provoquer la génération d’oscillations pathologiques, similaire aux oscillations enregistrées dans des tranches de cerveau épileptogène humaines. Dans la seconde partie de la thèse, nous avons étudié le rôle du bruit synaptique dans les cellules de Purkinje. Nous avons étudié l'effet de l'inhibition de la génération du potentiel d’action provoquée par injection de courant de bruit, un phénomène connu comme résonance stochastique inverse (RSI). Cet effet a déjà été trouvé dans des modèles neuronaux, et nous avons fournis sa première validation expérimentale. Nous avons trouvé que les cellules de Purkinje dans des tranches de cerveau peuvent être efficacement inhibées par des injectionsde bruit de courant. Cet effet est bien reproduit par le modèle phénoménologique adapté pour différentes cellules. En utilisant des méthodes de la théorie de l'information, nous avons montré que RSI prend en charge une transmission efficace de l'information des cellules de Purkinje simples suggérant son rôle pour les calculs du cervelet
In 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
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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.

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Les résultats de notre étude des capacités de modélisation théorique axées sur les approches phénoménologiques nucléaires dans le cadre de la théorie du champ-moyen sont présentés. On s’attend à ce qu’une théorie réaliste soit capable de prédire de manière satisfaisante les résultats des expériences à venir, c’est-à-dire avoir ce qu’on appelle un bon pouvoir prédictif. Pour étudier le pouvoir prédictif d’un modèle théorique, nous avons dû tenir compte non seulement des erreurs des données expérimentales, mais aussi des incertitudes issues des approximations du formalisme théorique et de l’existence de corrélations paramétriques. L’une des techniques centrales dans l’ajustement des paramètres est la solution de ce qu’on appelle le Problème Inverse. Les corrélations paramétriques induisent généralement un problème inverse mal-posé; elles doivent être étudiées et le modèle doit être régularisé. Nous avons testé deux types de hamiltoniens phénoménologiques réalistes montrant comment éliminer théoriquement et en pratique les corrélations paramétriques.Nous calculons les intervalles de confiance de niveau, les distributions d’incertitude des prédictions des modèles et nous avons montré comment améliorer les capacités de prédiction et la stabilité de la théorie
Results 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
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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.

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Dans ce travail de thèse, réalisé à l'IMFT, nous nous sommes intéressés aux écoulements turbulents diphasiques gaz-solides et plus particulièrement au phénomène de couplage inverse qui correspond à la modulation de la turbulence par la phase dispersée. Ce mécanisme est crucial pour les écoulements à forts chargements massiques. Dans cette thèse, nous avons considéré une turbulence homogène isotrope stationnaire sans gravité dans laquelle des particules sont suivies individuellement d'une façon Lagrangienne. La turbulence du fluide porteur est obtenue par des simulations directes (DNS). Les particules sont sphériques, rigides et d'une taille inférieure aux plus petites échelles de la turbulence. Leur densité est bien plus grande que la densité du fluide. Dans ce cadre, la force la plus importante agissant sur les particules est celle de traînée. Les interactions inter-particules ainsi que la gravité ne sont pas prises en compte. Pour modéliser ce type d'écoulement, une approche stochastique est utilisée pour laquelle l'accélération du fluide est modélisée par une équation de Langevin. L'originalité de ce travail est la prise en compte de l'effet de la modulation de la turbulence par un terme additionnel. Nous avons proposé deux modèles : une force de couplage moyenne qui est définie à partir des vitesses moyennes des phases, et une force instantanée qui est définie à l'aide du formalisme mésoscopique Eulérien. La fermeture des modèles s’appuie sur la fonction d’autocorrélation Lagrangienne et l’équation de transport de l’énergie cinétique. Les modèles sont testés en terme de prédiction de la vitesse de dérive et des corrélations fluide-particule. Les résultats montrent que le modèle moyen, plus simple, prend en compte les effets principaux du couplage inverse. Cependant, le problème de fermeture pratique est reporté sur la modélisation de l’échelle intégrale Lagrangienne et l’énergie cinétique de la turbulence du fluide vue par les particules
In 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
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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.

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Dans le cadre de l'élasticité linéaire 3D des microstructures complexes qui ne peuvent pas être simplement décrites en terme de constituants telles que des tissus biologiques, nous proposons, dans ce travail de recherche, une méthodologie d'identification expérimentale multi-échelle du champ stochastique d'élasticité apparent de la microstructure à l'échelle mésoscopique en utilisant des mesures de champ de déplacements aux échelles macroscopique et mésoscopique. On peut alors utiliser cette méthodologie dans le cadre de changement d'échelle pour obtenir des propriétés mécaniques à l'échelle macroscopique. Dans ce contexte, la question majeure est celle de l'identification expérimentale par résolution d'un problème statistique inverse de la modélisation stochastique introduite pour le champ d'élasticité apparent à l'échelle mésoscopique. Cette identification expérimentale permet non seulement de valider la modélisation mais encore de la rendre utile pour des matériaux existants ayant une microstructure complexe. Le présent travail de recherche est une contribution proposée dans ce cadre pour lequel l'expérimentation et validation expérimentale basée sur des mesures simultanées d'imagerie de champ aux échelles macroscopique et mésoscopique sont faites sur de l'os cortical
In 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
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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.

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Les voies ferrées sont de plus en plus sollicitées: le nombre de trains à grande vitesse, leur vitesse et leur charge ne cessent d'augmenter, ce qui contribue à la formation de défauts de géométrie sur la voie. En retour, ces défauts de géométrie influencent la réponse dynamique du train et dégradent les conditions de confort. Pour garantir de bonnes conditions de confort, les entreprises ferroviaires réalisent des opérations de maintenance de la voie, qui sont très coûteuses. Ces entreprises ont donc intérêt à prévoir l'évolution temporelle des défauts de géométrie de la voie pour anticiper les opérations de maintenance, et ainsi réduire les coûts de maintenance et améliorer les conditions de transport. Dans cette thèse, on analyse l'évolution temporelle d'une portion de voie par un indicateur vectoriel sur la dynamique du train. Pour la portion de voie choisie, on construit un modèle stochastique local des défauts de géométrie de la voie à partir d'un modèle global des défauts de géométrie et de big data de défauts mesurés par un train de mesure. Ce modèle stochastique local prend en compte la variabilité des défauts de géométrie de la voie et permet de générer des réalisations des défauts pour chaque temps de mesure. Après avoir validé le modèle numérique de la dynamique du train, les réponses dynamiques du train sur la portion de voie mesurée sont simulées numériquement en utilisant le modèle stochastique local des défauts de géométrie. Un indicateur dynamique, vectoriel et aléatoire, est introduit pour caractériser la réponse dynamique du train sur la portion de voie. Cet indicateur dynamique est construit de manière à prendre en compte les incertitudes de modèle dans le modèle numérique de la dynamique du train. Pour identifier le modèle stochastique des défauts de géométrie et pour caractériser les incertitudes de modèle, des méthodes stochastiques avancées, comme par exemple la décomposition en chaos polynomial ou le maximum de vraisemblance multidimensionnel, sont appliquées à des champs aléatoires non gaussiens et non stationnaires. Enfin, un modèle stochastique de prédiction est proposé pour prédire les quantités statistiques de l'indicateur dynamique, ce qui permet d'anticiper le besoin en maintenance. Ce modèle est construit en utilisant les résultats de la simulation de la dynamique du train et consiste à utiliser un modèle non stationnaire de type filtre de Kalman avec une condition initiale non gaussienne
Railways 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
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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.

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Dans le cadre de la théorie de l'élasticité linéaire, la modélisation et simulation numérique du comportement mécanique des matériaux hétérogènes à microstructure aléatoire complexe soulèvent de nombreux défis scientifiques à différentes échelles. Bien qu'à l'échelle macroscopique, ces matériaux soient souvent modélisés comme des milieux homogènes et déterministes, ils sont non seulement hétérogènes et aléatoires à l'échelle microscopique, mais ils ne peuvent généralement pas non plus être explicitement décrits par les propriétés morphologiques et mécaniques locales de leurs constituants. Par conséquent, une échelle mésoscopique est introduite entre l'échelle macroscopique et l'échelle mésoscopique, pour laquelle les propriétés mécaniques d'un tel milieu élastique linéaire aléatoire sont décrites par un modèle stochastique prior non-gaussien paramétré par un nombre faible ou modéré d'hyperparamètres inconnus. Afin d'identifier ces hyperparamètres, une méthodologie innovante a été récemment proposée en résolvant un problème statistique inverse multi-échelle en utilisant uniquement des données expérimentales partielles et limitées aux deux échelles macroscopique et mésoscopique. Celui-ci a été formulé comme un problème d'optimisation multi-objectif qui consiste à minimiser une fonction-coût multi-objectif (à valeurs vectorielles) définie par trois indicateurs numériques correspondant à des fonctions-coût mono-objectif (à valeurs scalaires) permettant de quantifier et minimiser des distances entre les données expérimentales multi-échelles mesurées simultanément aux deux échelles macroscopique et mésoscopique sur un seul échantillon soumis à un essai statique, et les solutions des modèles numériques déterministe et stochastique utilisés pour simuler la configuration expérimentale multi-échelle sous incertitudes. Ce travail de recherche vise à contribuer à l'amélioration de la méthodologie d'identification inverse statistique multi-échelle en terme de coût de calcul, de précision et de robustesse en introduisant (i) une fonction-coût mono-objectif (indicateur numérique) supplémentaire à l'échelle mésoscopique quantifiant la distance entre la(les) longueur(s) de corrélation spatiale des champs expérimentaux mesurés et celle(s) des champs numériques calculés, afin que chaque hyperparamètre du modèle stochastique prior ait sa propre fonction-coût mono-objectif dédiée, permettant ainsi d'éviter d'avoir recours à l'algorithme d'optimisation global (algorithme génétique) utilisé précédemment et de le remplacer par un algorithme plus performant en terme d'efficacité numérique, tel qu'un algorithme itératif de type point fixe, pour résoudre le problème d'optimisation multi-objectif avec un coût de calcul plus faible, et (ii) une représentation stochastique ad hoc des hyperparamètres impliqués dans le modèle stochastique prior du champ d'élasticité aléatoire à l'échelle mésoscopique en les modélisant comme des variables aléatoires, pour lesquelles les distributions de probabilité peuvent être construites en utilisant le principe du maximum d'entropie sous un ensemble de contraintes définies par les informations objectives et disponibles, et dont les hyperparamètres peuvent être déterminés à l'aide de la méthode d'estimation du maximum de vraisemblance avec les données disponibles, afin d'améliorer à la fois la robustesse et la précision de la méthode d'identification inverse du modèle stochastique prior. En parallèle, nous proposons également de résoudre le problème d'optimisation multi-objectif en utilisant l’apprentissage automatique par des réseaux de neurones artificiels. Finalement, la méthodologie améliorée est tout d'abord validée sur un matériau virtuel fictif dans le cadre de l'élasticité linéaire en 2D contraintes planes et 3D, puis illustrée sur un matériau biologique hétérogène réel (os cortical de bœuf) en élasticité linéaire 2D contraintes planes
Within 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
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10

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.

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Cette thèse vise à concevoir et analyser des modèles de dynamique des populations dédiés à la dynamique des cellules somatiques durant les premiers stades de la croissance du follicule ovarien. Les comportements des modèles sont analysés par des approches théoriques et numériques, et les valeurs des paramètres sont calibrées en proposant des stratégies de maximum de vraisemblance adaptées à notre jeu de données spécifique. Un modèle stochastique non linéaire, qui tient compte de la dynamique conjointe entre deux types cellulaires (précurseur et prolifératif), est dédié à l'activation de la croissance folliculaire. Une approche rigoureuse de projection par états finis est mise en œuvre pour caractériser l'état du système à l'extinction et calculer le temps d'extinction des cellules précurseurs. Un modèle linéaire multi-type structuré en âge, appliquée à la population de cellules prolifératives, est dédié à la croissance folliculaire précoce. Les différents types correspondent ici aux positions spatiales des cellules. Ce modèle est de type décomposable ; les transitions sont unidirectionnelles du premier vers le dernier type. Nous prouvons la convergence en temps long du modèle stochastique de Bellman-Harris et de l'équation de McKendrick-VonFoerster multi-types. Nous adaptons les résultats existants dans le cas où le théorème de Perron-Frobenius ne s'applique pas, et nous obtenons des formules analytiques explicites pour les moments asymptotiques des nombres de cellules et de la distribution stationnaire en âge. Nous étudions également le caractère bien posé du problème inverse associé au modèle déterministe
This 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
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Books on the topic "Stochastic inverse modeling"

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Ravalec, Mickaële Le. Inverse stochastic modeling of flow in porous media: Applications to reservoir characterization. Paris: Editions Technip, 2005.

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Inverse Stochastic Modeling of Flow in Porous Media: Application to Reservoir Characterization (Ifp Publications). Technip, 2005.

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Book chapters on the topic "Stochastic inverse modeling"

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Kitanidis, Peter K. "On stochastic inverse modeling." In Subsurface Hydrology: Data Integration for Properties and Processes, 19–30. Washington, D. C.: American Geophysical Union, 2007. http://dx.doi.org/10.1029/171gm04.

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Sun, Ne-Zheng. "The Stochastic Method for Solving Inverse Problems." In Inverse Problems in Groundwater Modeling, 141–93. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-1970-4_7.

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Gómez-Hernández, J. Jaime, and Jianlin Fu. "Blocking Markov Chain Monte Carlo Schemes for Inverse Stochastic Hydrogeological Modeling." In Quantitative Geology and Geostatistics, 121–26. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-2322-3_11.

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Wilson, J. D., T. K. Flesch, and B. P. Crenna. "Estimating Surface-Air Gas Fluxes by Inverse Dispersion Using a Backward Lagrangian Stochastic Trajectory Model." In Lagrangian Modeling of the Atmosphere, 149–62. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/2012gm001269.

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Fu, Jianlin, J. Jaime Gómez-Hernández, and Song Du. "A Gradient-Based Blocking Markov Chain Monte Carlo Method for Stochastic Inverse Modeling." In Geostatistics Valencia 2016, 777–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46819-8_53.

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Buldygin, Valeriĭ V., Karl-Heinz Indlekofer, Oleg I. Klesov, and Josef G. Steinebach. "Asymptotically Quasi-inverse Functions." In Probability Theory and Stochastic Modelling, 229–310. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99537-3_7.

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Kiselev, O. M. "Stochastic Properties of an Inverted Pendulum on a Wheel on a Soft Surface." In 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.

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Lobo-Ferreira, J. P. "On the Application of Stochastic Inverse Modelling to the Fractured Semi-Confined Aquifer of Bagueixe, Portugal." In geoENV I — Geostatistics for Environmental Applications, 39–50. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1675-8_4.

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Fernández-Martínez, Juan Luis, Esperanza García-Gonzalo, Saras Saraswathi, Robert Jernigan, and Andrzej Kloczkowski. "Particle Swarm Optimization: A Powerful Family of Stochastic Optimizers. Analysis, Design and Application to Inverse Modelling." In 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.

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Lobo-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." In 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.

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Conference papers on the topic "Stochastic inverse modeling"

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Jones, N. L., J. I. Green, and J. R. Walker. "Stochastic Inverse Modeling for Capture Zone Analysis." In 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.

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Serino, Gennaro, T. Magin, Patrick Rambaud, and Fabio Pinna. "Statistical inverse analysis and stochastic modeling of transition." In 43rd AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2883.

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Kaulakys, Bronislovas, and Miglius Alaburda. "Modeling the inverse cubic distributions by nonlinear stochastic differential equations." In 2011 21st International Conference on Noise and Fluctuations (ICNF). IEEE, 2011. http://dx.doi.org/10.1109/icnf.2011.5994380.

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Jorge, Ariosto, Patricia Lopes, and Sebastião Cunha. "Modeling of an Inverse Problem for Damage Detection Using Stochastic Optimization Techniques." In 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.

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Pace, F., A. Santilano, A. Godio, and A. Manzella. "Stochastic Inverse Modeling of Magnetotelluric Data from the Larderello-Travale Geothermal Area (Italy)." In 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.

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Guadagnini, A., A. Russian, M. Riva, E. R. Russo, and M. A. Chiaramonte. "Quantification of Uncertainties of Fracture Permeability Via Mud Loss Information and Inverse Stochastic Modeling." In 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901624.

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Ma, Rui, and John B. Ferris. "Terrain Gridding Using a Stochastic Weighting Function." In 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.

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The development of new stochastic terrain gridding methods are necessitated by new tire and vehicle modeling applications. Currently, grid node locations in the horizontal plane are assumed to be known and only the uncertainty in the vertical height estimates is modeled. This work modifies the current practice of weighting the importance of a particular measured data point (the terrain height at some horizontal location) by the inverse distance between the grid node and that point. A new weighting function is developed to account for the error in the horizontal position of the grid nodes. The geometry of the problem is described and the probability distribution is developed in steps. Although the solution cannot be determined in closed form, an estimate of the median distance is developed within 1% error. This more complete stochastic definition of the terrain can then be used for advanced tire modeling and vehicle simulation.
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Markov, Pavel. "New Technology for Inverse Problem Solving of Digital Core Model Construction Using Stochastic Modeling and Particle Swarm Optimization." In SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201944-ms.

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Markov, Pavel. "New Technology for Inverse Problem Solving of Digital Core Model Construction Using Stochastic Modeling and Particle Swarm Optimization (Russian)." In SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201944-ru.

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Salama, Mostafa, and Vladimir V. Vantsevich. "Mechatronics Implementation of Inverse Dynamics-Based Controller for an Off-Road UGV." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51010.

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This paper presents a project developed at the University of Alabama at Birmingham (UAB) aimed to design, implement, and test an off-road Unmanned Ground Vehicle (UGV) with individually controlled four drive wheels that operate in stochastic terrain conditions. An all-wheel drive off-road UGV equipped with individual electric dc motors for each wheel offers tremendous potential to control the torque delivered to each individual wheel in order to maximize UGV slip efficiency by minimizing slip power losses. As previous studies showed, this can be achieved by maintaining all drive wheels slippages the same. Utilizing this approach, an analytical method to control angular velocities of all wheels was developed to provide the same slippages of the four wheels. This model-based method was implemented in an inverse dynamics-based control algorithm of the UGV to overcome stochastic terrain conditions and minimize wheel slip power losses and maintain a given velocity profile. In this paper, mechanical and electrical components and control algorithm of the UGV are described in order to achieve the objective. Optical encoders built-in each dc motor are used to measure the actual angular velocity of each wheel. A fifth wheel rotary encoder sensor is attached to the chassis to measure the distance travel and estimate the longitudinal velocity of the UGV. In addition, the UGV is equipped with four electric current sensors to measure the current draw from each dc motor at various load conditions. Four motor drivers are used to control the dc motors using National Instruments single-board RIO controller. Moreover, power system diagrams and controller pinout connections are presented in detail and thus explain how all these components are integrated in a mechatronic system. The inverse dynamics control algorithm is implemented in real-time to control each dc motors individually. The integrated mechatronics system is distinguished by its robustness to stochastic external disturbances as shown in the previous papers. It also shows a promising adaptability to disturbances in wheel load torques and changes in stochastic terrain properties. The proposed approach, modeling and hardware implementation opens up a new way to the optimization and control of both unmanned ground vehicle dynamics and vehicle energy efficiency by optimizing and controlling individual power distribution to the drive wheels.
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Reports on the topic "Stochastic inverse modeling"

1

GÓMEZ-HERNÁNDEZ, J. Jaime, Haiyan ZHOU, Liangping LI, and Harrie-Jan HENDRICKS FRANSSEN. Abnormal Inverse Stochastic Modeling. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0131.

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