Academic literature on the topic 'Processus modeling'
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Journal articles on the topic "Processus modeling":
Laloë, Francis. "Modelling sustainability: from applied to involved modeling." Social Science Information 46, no. 1 (March 2007): 87–107. http://dx.doi.org/10.1177/0539018407073659.
Goujon Belghit, Anne, Jocelyn Husser, and Delphine Lacaze. "Les cadres de la fonction RH face au processus de lancement d’alerte." @GRH N° 48, no. 3 (August 18, 2023): 161–84. http://dx.doi.org/10.3917/grh.048.0161.
Bonneton, Domitille, and Christelle Chaplais-Chouvier. "« En théorie, c’est binaire » : comment les auditeurs apprennent-ils à résoudre des dilemmes éthiques ?" @GRH N° 48, no. 3 (August 18, 2023): 113–32. http://dx.doi.org/10.3917/grh.048.0113.
de Malherbe, Etienne. "MODELING PRIVATE EQUITY FUNDS AND PRIVATE EQUITY COLLATERALISED FUND OBLIGATIONS." International Journal of Theoretical and Applied Finance 07, no. 03 (May 2004): 193–230. http://dx.doi.org/10.1142/s0219024904002359.
Mainhagu, Sébastien. "Les attitudes de résilience de carrière dans le contexte de crise sanitaire." @GRH N° 48, no. 3 (August 18, 2023): 65–85. http://dx.doi.org/10.3917/grh.048.0065.
Nimeskern, Lola, Alix Brouillon, and Didier Vanoni. "II. La dimension inclusive de l’habitat : de l’identification des besoins au processus d’attribution." Recherche sociale N° 240, no. 4 (December 20, 2023): 18–36. http://dx.doi.org/10.3917/recsoc.240.0018.
Foigel, Maya, and Renato Mezan. "De la non-conformité à la diversité de genre." Adolescence T.41 n° 2, no. 2 (October 20, 2023): 407–19. http://dx.doi.org/10.3917/ado.112.0407.
Pechikoff, Stéphanie. "Penser les transidentités au prisme de l’adolescence." Adolescence T.41 n° 2, no. 2 (October 20, 2023): 325–37. http://dx.doi.org/10.3917/ado.112.0325.
Ewanzo, Lydia, and Johann Jung. "De l’effacement de soi à l’appropriation subjective." Adolescence T.41 n° 2, no. 2 (October 20, 2023): 463–76. http://dx.doi.org/10.3917/ado.112.0463.
Hafhouf-Lacôte, Hindi, and Julia Neyroud. "Dispositif « triple résonance » : accueillir le trauma." Adolescence T.41 n° 2, no. 2 (October 20, 2023): 477–87. http://dx.doi.org/10.3917/ado.112.0477.
Dissertations / Theses on the topic "Processus modeling":
Dávila-Felipe, Miraine. "Pathwise decompositions of Lévy processes : applications to epidemiological modeling." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066651.
This dissertation is devoted to the study of some pathwise decompositions of spectrally positive Lévy processes, and duality relationships for certain (possibly non-Markovian) branching processes, driven by the use of the latter as probabilistic models of epidemiological dynamics. More precisely, we model the transmission tree of a disease as a splitting tree, i.e. individuals evolve independently from one another, have i.i.d. lifetimes (periods of infectiousness) that are not necessarily exponential, and give birth (secondary infections) at a constant rate during their lifetime. The incidence of the disease under this model is a Crump-Mode-Jagers process (CMJ); the overarching goal of the two first chapters is to characterize the law of this incidence process through time, jointly with the partially observed (inferred from sequence data) transmission tree. In Chapter I we obtain a description, in terms of probability generating functions, of the conditional likelihood of the number of infectious individuals at multiple times, given the transmission network linking individuals that are currently infected. In the second chapter, a more elegant version of this characterization is given, passing by a general result of invariance under time reversal for a class of branching processes. Finally, in Chapter III we are interested in the law of the (sub)critical branching process seen from its extinction time. We obtain a duality result that implies in particular the invariance under time reversal from their extinction time of the (sub)critical CMJ processes and the excursion away from 0 of the critical Feller diffusion (the width process of the continuum random tree)
Suri, Kunal. "Modeling the internet of things in configurable process models." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL005/document.
On the one hand, a growing number of multi-national organizations have embraced the Process-Aware Information Systems (PAIS) to reap the benefits of using streamlined processes that are based on predefined models, also called as Business Process (BP) models. However, today's dynamic business environment demands flexibility and systematic reuse of BPs, which is provided by the use of Configurable Process Models (CPMs). It avoids the development of processes from scratch, which is both time-consuming and error-prone, and facilitates the sharing of a family of BP variants that can be customized based on concrete business requirements. On the other hand, the adoption of the Internet of Things (IoT) resources in various cross-organizational BPs is also on a rise. However, to attain the desired business value, these IoT resources must be used efficiently. These IoT devices are heterogeneous due to their diverse properties and manufactures (proprietary standards), which leads to issues related to interoperability. Further, being resource-constrained, they need to be allocated (and consumed) keeping in the mind relevant constraints such as energy cost, computation cost, to avoid failures during the time of their consumption in the processes. Thus, it is essential to explicitly model the IoT resource perspective in the BP models during the process design phase. In the literature, various research works in Business Process Management (BPM) domain are usually focused on the control-flow perspective. While there do exist some approaches that focus on the resource perspective, they are typically dedicated to the human resource perspective. Thus, there is limited work on integrating the IoT resource perspective into BPs, without any focus on solving issues related to heterogeneity in IoT domain. Likewise, in the context of CPMs, there is no configuration support to model IoT resource variability at the CPM level. This variability is a result of specific IoT resource features such as Shareability and Replication that is relevant in the context of BPs. In this thesis, we address the aforementioned limitations by proposing an approach to integrate IoT perspective in the BPM domain and to support the development of IoT-Aware CPMs. This work contributes in the following manner: (1) it provides a formal description of the IoT resource perspective and its relationships with the BPM domain using semantic technology and (2) it provides novel concepts to enable configurable IoT resource allocation in CPMs. To validate our approach and to show its feasibility, we do the following: (1) implement proof of concept tools that assist in the development of IoT-aware BPs and IoT-aware CPMs and (2) perform experiments on the process model datasets. The experimentation results show the effectiveness of our approach and affirm its feasibility
Beccuti, Marco. "Modeling and analisys of probabilistic system : Formalism and efficient algorithm." Paris 9, 2008. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2008PA090060.
Yongsiriwit, Karn. "Modeling and mining business process variants in cloud environments." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL002/document.
More and more organizations are adopting cloud-based Process-Aware Information Systems (PAIS) to manage and execute processes in the cloud as an environment to optimally share and deploy their applications. This is especially true for large organizations having branches operating in different regions with a considerable amount of similar processes. Such organizations need to support many variants of the same process due to their branches' local culture, regulations, etc. However, developing new process variant from scratch is error-prone and time consuming. Motivated by the "Design by Reuse" paradigm, branches may collaborate to develop new process variants by learning from their similar processes. These processes are often heterogeneous which prevents an easy and dynamic interoperability between different branches. A process variant is an adjustment of a process model in order to flexibly adapt to specific needs. Many researches in both academics and industry are aiming to facilitate the design of process variants. Several approaches have been developed to assist process designers by searching for similar business process models or using reference models. However, these approaches are cumbersome, time-consuming and error-prone. Likewise, such approaches recommend entire process models which are not handy for process designers who need to adjust a specific part of a process model. In fact, process designers can better develop process variants having an approach that recommends a well-selected set of activities from a process model, referred to as process fragment. Large organizations with multiple branches execute BP variants in the cloud as environment to optimally deploy and share common resources. However, these cloud resources may be described using different cloud resources description standards which prevent the interoperability between different branches. In this thesis, we address the above shortcomings by proposing an ontology-based approach to semantically populate a common knowledge base of processes and cloud resources and thus enable interoperability between organization's branches. We construct our knowledge base built by extending existing ontologies. We thereafter propose an approach to mine such knowledge base to assist the development of BP variants. Furthermore, we adopt a genetic algorithm to optimally allocate cloud resources to BPs. To validate our approach, we develop two proof of concepts and perform experiments on real datasets. Experimental results show that our approach is feasible and accurate in real use-cases
Domingues, Rémi. "Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS473.pdf.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. While numerous novelty detection methods were designed to model continuous numerical data, tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) a survey of state-of-the-art novelty detection methods applied to mixed-type data, including extensive scalability, memory consumption and robustness tests (ii) a survey of state-of-the-art novelty detection methods suitable for sequence data (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes. The learning of this last model is made tractable and scalable through the use of random feature approximations and stochastic variational inference. The method is suitable for large-scale novelty detection problems and data with mixed-type features. The experiments indicate that the proposed model achieves competitive results with state-of-the-art novelty detection methods
Petit, Sébastien. "Improved Gaussian process modeling : Application to Bayesian optimization." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG063.
This manuscript focuses on Bayesian modeling of unknown functions with Gaussian processes. This task arises notably for industrial design, with numerical simulators whose computation time can reach several hours. Our work focuses on the problem of model selection and validation and goes in two directions. The first part studies empirically the current practices for stationary Gaussian process modeling. Several issues on Gaussian process parameter selection are tackled. A study of parameter selection criteria is the core of this part. It concludes that the choice of a family of models is more important than that of the selection criterion. More specifically, the study shows that the regularity parameter of the Matérn covariance function is more important than the choice of a likelihood or cross-validation criterion. Moreover, the analysis of the numerical results shows that this parameter can be selected satisfactorily by the criteria, which leads to a practical recommendation. Then, particular attention is given to the numerical optimization of the likelihood criterion. Observing important inconsistencies between the different libraries available for Gaussian process modeling like Erickson et al. (2018), we propose elementary numerical recipes making it possible to obtain significant gains both in terms of likelihood and model accuracy. Finally, the analytical formulas for computing cross-validation criteria are revisited under a new angle and enriched with similar formulas for the gradients. This last contribution aligns the computational cost of a class of cross-validation criteria with that of the likelihood. The second part presents a goal-oriented methodology. It is designed to improve the accuracy of the model in an (output) range of interest. This approach consists in relaxing the interpolation constraints on a relaxation range disjoint from the range of interest. We also propose an approach for automatically selecting the relaxation range. This new method can implicitly manage potentially complex regions of interest in the input space with few parameters. Outside, it learns non-parametrically a transformation improving the predictions on the range of interest. Numerical simulations show the benefits of the approach for Bayesian optimization, where one is interested in low values in the minimization framework. Moreover, the theoretical convergence of the method is established under some assumptions
Ben, salem Malek. "Model selection and adaptive sampling in surrogate modeling : Kriging and beyond." Thesis, Lyon, 2018. https://tel.archives-ouvertes.fr/tel-03097719.
Surrogate models are used to replace an expensive-to-evaluate function to speed-up the estimation of a feature of a given function (optimum, contour line, …). Three aspects of surrogate modeling are studied in the work:1/ We proposed two surrogate model selection algorithms. They are based on a novel criterion called the penalized predictive score. 2/ The main advantage of probabilistic approach is that it provides a measure of uncertainty associated with the prediction. This uncertainty is an efficient tool to construct strategies for various problems such as prediction enhancement, optimization or inversion. We defined a universal approach for uncertainty quantification that could be applied for any surrogate model. It is based on a weighted empirical probability measure supported by cross-validation sub-models’ predictions.3/ We present the so-called Split-and-Doubt algorithm that performs sequentially both feature estimation and dimension reduction
Larvaron, Benjamin. "Modeling battery health degradation with uncertainty quantification." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0028.
With the acceleration of climate change, significant measures must be taken to decarbonize the economy. This includes a transformation of the transportation and energy production sectors. These changes increase the use of electrical energy and raise the need for storage, particularly through Lithium-ion batteries.In this thesis, we focus on modeling battery health degradation. To quantify the risks associated with performance guarantees, uncertainties must be taken into account. Degradation is a complex phenomenon involving various interacting physical mechanisms. It varies depending on the battery type and usage conditions. We first addressed the issue of the temporal degradation under a reference experimental condition using a data-driven approach based on Gaussian processes. This approach allows for learning complex models while incorporating uncertainty quantification. Building upon the state-of-the art, we proposed an adaptation of Gaussian process regression. By designing appropriate kernels, the model explicitly considers performance variability among batteries. However, Gaussian process regression generally relies on a stationarity assumption, which is too restrictive to account for uncertainty evolution over time. Therefore, we have leveraged the broader framework of chained Gaussian process regression, based on variational inference. With a suitable choice of likelihood function, this framework allows for adjusting a non-parametric model of the evolution of the variability among batteries, significantly improving uncertainty quantification. While this approach yields a model that fits observed cycles well, it does not generalize effectively to predict future degradation with consistent physical behaviors. Specifically, monotonicity and concavity of degradation curves are not always preserved. To address this, we proposed an approach to incorporate these constraints into chained Gaussian process regression. As a result, we have enhanced predictions over several hundred cycles, potentially reducing the necessary battery testing time—a significant cost for manufacturers. We then expanded the problem to account for the effect of experimental conditions on battery degradation. Initially, we attempted to adapt Gaussian process-based methods by including experimental factors as additional explanatory variables. This approach yielded interesting results in cases with similar degradation conditions. However, for more complex settings, the results became inconsistent with physical knowledge and were no longer usable. As a result, we proposed an alternative two-step approach, separating the temporal evolution from the effect of factors. In the first step, temporal evolution was modeled using the previously mentioned Gaussian process methods. The second, more complex step utilized the results from the previous stage—Gaussian distributions—to learn a model of experimental conditions. This required a regression approach for complex data. We suggest using Wasserstein conditional barycenters, which are well-suited for distribution cases. Two models were introduced. The first model, within the structured regression framework, incorporates a physical degradation model. The second model, using Fréchet regression, improves results by interpolating experimental conditions and accounting for multiple experimental factors
Suri, Kunal. "Modeling the internet of things in configurable process models." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL005.
On the one hand, a growing number of multi-national organizations have embraced the Process-Aware Information Systems (PAIS) to reap the benefits of using streamlined processes that are based on predefined models, also called as Business Process (BP) models. However, today's dynamic business environment demands flexibility and systematic reuse of BPs, which is provided by the use of Configurable Process Models (CPMs). It avoids the development of processes from scratch, which is both time-consuming and error-prone, and facilitates the sharing of a family of BP variants that can be customized based on concrete business requirements. On the other hand, the adoption of the Internet of Things (IoT) resources in various cross-organizational BPs is also on a rise. However, to attain the desired business value, these IoT resources must be used efficiently. These IoT devices are heterogeneous due to their diverse properties and manufactures (proprietary standards), which leads to issues related to interoperability. Further, being resource-constrained, they need to be allocated (and consumed) keeping in the mind relevant constraints such as energy cost, computation cost, to avoid failures during the time of their consumption in the processes. Thus, it is essential to explicitly model the IoT resource perspective in the BP models during the process design phase. In the literature, various research works in Business Process Management (BPM) domain are usually focused on the control-flow perspective. While there do exist some approaches that focus on the resource perspective, they are typically dedicated to the human resource perspective. Thus, there is limited work on integrating the IoT resource perspective into BPs, without any focus on solving issues related to heterogeneity in IoT domain. Likewise, in the context of CPMs, there is no configuration support to model IoT resource variability at the CPM level. This variability is a result of specific IoT resource features such as Shareability and Replication that is relevant in the context of BPs. In this thesis, we address the aforementioned limitations by proposing an approach to integrate IoT perspective in the BPM domain and to support the development of IoT-Aware CPMs. This work contributes in the following manner: (1) it provides a formal description of the IoT resource perspective and its relationships with the BPM domain using semantic technology and (2) it provides novel concepts to enable configurable IoT resource allocation in CPMs. To validate our approach and to show its feasibility, we do the following: (1) implement proof of concept tools that assist in the development of IoT-aware BPs and IoT-aware CPMs and (2) perform experiments on the process model datasets. The experimentation results show the effectiveness of our approach and affirm its feasibility
Azzi, Soumaya. "Surrogate modeling of stochastic simulators." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT009.
This thesis is a contribution to the surrogate modeling and the sensitivity analysis on stochastic simulators. Stochastic simulators are a particular type of computational models, they inherently contain some sources of randomness and are generally computationally prohibitive. To overcome this limitation, this manuscript proposes a method to build a surrogate model for stochastic simulators based on Karhunen-Loève expansion. This thesis also aims to perform sensitivity analysis on such computational models. This analysis consists on quantifying the influence of the input variables onto the output of the model. In this thesis, the stochastic simulator is represented by a stochastic process, and the sensitivity analysis is then performed on the differential entropy of this process.The proposed methods are applied to a stochastic simulator assessing the population’s exposure to radio frequency waves in a city. Randomness is an intrinsic characteristic of the stochastic city generator. Meaning that, for a set of city parameters (e.g. street width, building height and anisotropy) does not define a unique city. The context of the electromagnetic dosimetry case study is presented, and a surrogate model is built. The sensitivity analysis is then performed using the proposed method
Books on the topic "Processus modeling":
Nelson, Randolph. Probability, stochastic processes, and queueing theory: The mathematics of computer performance modeling. New York: Springer-Verlag, 1995.
King, Alan J. Modeling with Stochastic Programming. New York, NY: Springer New York, 2012.
Birolini, Alessandro. On the use of stochastic processes in modeling reliability problems. Berlin: Springer-Verlag, 1985.
Gunter, Bolch, ed. Queueing networks and Markov chains: Modeling and performance evaluation with computer science applications. New York: Wiley, 1998.
Gigch, John P. Van. System design modeling and metamodeling. New York: Plenum Press, 1991.
Bequette, B. Wayne. Process dynamics: Modeling, analysis, and simulation. Upper Saddle River, N.J: Prentice Hall PTR, 1998.
Georgiadis, Michael C., Julio R. Banga, and Efstratios N. Pistikopoulos. Dynamic process modeling. Weinheim: Wiley-VCH, 2011.
B, Gershwin S., ed. Analysis and modeling of manufacturing systems. Boston: Kluwer Academic Publishers, 2003.
Bill, Chu Bei-Tseng, and Chen Su-shing, eds. Intelligent modeling, diagnosis and control of manufacturing processes. Singapore: World Scientific, 1992.
Gershwin, Stanley B. Analysis and Modeling of Manufacturing Systems. Boston, MA: Springer US, 2003.
Book chapters on the topic "Processus modeling":
Lanchier, Nicolas. "Branching processes." In Stochastic Modeling, 93–99. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50038-6_6.
Simonovits, András. "Demographic processes." In Modeling Pension Systems, 65–75. London: Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230597693_8.
Blanco-Castañeda, Liliana, and Viswanathan Arunachalam. "Branching Processes." In Applied Stochastic Modeling, 95–126. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31282-3_4.
Serovajsky, Simon. "Wave processes." In Mathematical Modelling, 213–38. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003035602-12.
Moller, Faron, and Georg Struth. "Modelling Processes." In Undergraduate Topics in Computer Science, 279–307. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-84800-322-4_12.
Lanchier, Nicolas. "Stochastic processes: general definition." In Stochastic Modeling, 59–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50038-6_4.
Bandura, Albert. "Analysis of Modeling Processes." In Psychological Modeling, 1–62. Classic edition. | New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003110156-1.
Paul, Wolfgang, and Jörg Baschnagel. "Modeling the Financial Market." In Stochastic Processes, 163–235. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00327-6_5.
Ho, Teh C. "Modeling Refining Processes." In Springer Handbook of Petroleum Technology, 841–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49347-3_27.
Hřebíček, J., and T. Pitner. "Modeling Social Processes." In Solving Problems in Scientific Computing Using Maple and MATLAB®, 351–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18873-2_24.
Conference papers on the topic "Processus modeling":
Oberhauser, Roy. "Adapting Processes via Adaptation ProcessesA Flexible and Cloud-Capable Adaptation Approach for Dynamic Business Process Management." In Fifth International Symposium on Business Modeling and Software Design. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005885000090018.
Batteh, John, Jesse Gohl, James Ferri, Quang Le, Bill Glandorf, Bob Sherman, and Rudolfs Opmanis. "Material Production Process Modeling with Automated Modelica Models from IBM Rational Rhapsody." In American Modelica Conference 2022, Dallas, October 26-28. Linköping University Electronic Press, 2023. http://dx.doi.org/10.3384/ecp21186158.
Baharev, Ali, and Arnold Neumaier. "Chemical Process Modeling in Modelica." In 9th International MODELICA Conference, Munich, Germany. Linköping University Electronic Press, 2012. http://dx.doi.org/10.3384/ecp12076955.
"Business Processes, Process Logic and Information ArchitectureA Tentative Case Study." In Third International Symposium on Business Modeling and Software Design. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004773900430053.
Bren, David. "Radiative processes calculation in plasma fiber." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386879.
"DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES." In Special Session on Computationally Efficient Simulation-Driven Engineering Design Optimization and Modeling. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003647804920501.
Mohamad Noor, Papamichail, and Warboys. "Process modelling for online communications in tendering processes." In Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat No 03CH37412) EURMIC-03. IEEE, 2003. http://dx.doi.org/10.1109/eurmic.2003.1231562.
Scholz-Reiter, Bernd, Daniel Rippel, and Steffen Sowade. "Limitations in modeling autonomous logistic processes: Challenges and solutions in business process modeling." In 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM). IEEE, 2011. http://dx.doi.org/10.1109/isam.2011.5942324.
Zolnikov, Konstantin, K. Tapero, Valeriy Suhanov, and D. Chernov. "RESULTS OF TESTS OF CONTROL SYSTEMS ON EXPOSURE TO HEAVY CHARGED PARTICLES." In Modern aspects of modeling systems and processes. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2021. http://dx.doi.org/10.34220/mamsp_257-263.
Zolnikov, Konstantin, Svetlana Evdokimova, A. Yagodkin, Sergey Grechanyy, and P. Parmon. "PRODUCT LIFE ASSESSMENT METHODOLOGY UNDER RADIATION EXPOSURE." In Modern aspects of modeling systems and processes. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2021. http://dx.doi.org/10.34220/mamsp_247-252.
Reports on the topic "Processus modeling":
Buckmaster, John. Modeling of Physical Processes. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada408985.
Ratcliff, Roger. Modeling Perceptual Decision Processes. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada609771.
Buchmaster. Modeling of Physical Processes. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada384825.
Kellner, Marc I., and Gregory A. Hansen. Software Process Modeling. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada197137.
Rhee, M., R. Becker, R. Couch, and M. Li. Modeling Production Plant Forming Processes. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/918410.
Weatherly, Georges L. Modeling Coastal Sediment Transport Processes. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada300247.
Maxey, Martin. Modeling Mesoscale Processes of Scalable Synthesis. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1496226.
Beyeler, Walter E., Mercy B. DeMenno, and Patrick D. Finley. Modeling veterans healthcare administration disclosure processes :. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1096264.
Werner, Brad. Modeling Nearshore Processes as Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416942.
Biaggne, Austin Robert, Michael D. McMurtrey, Joseph Louis Bass, and Larry K. Aagesen Jr. Modeling Sintering Processes of Nanoparticle Inks. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1546728.