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Статті в журналах з теми "Simulation différentiable"
Livet, Pierre. "La simulation : des déplacements de notre épistémologie." Nouvelles perspectives en sciences sociales 5, no. 2 (July 6, 2010): 51–57. http://dx.doi.org/10.7202/044074ar.
Повний текст джерелаДисертації з теми "Simulation différentiable"
Benacer, Rachid. "Contribution à l'étude des algorithmes de l'optimisation non convexe et non différentiable." Phd thesis, Grenoble 1, 1986. http://tel.archives-ouvertes.fr/tel-00320986.
Повний текст джерелаTaralova-Roux, Ina. "Etude de la transmission MICDIF à caractéristique non différentiable à l'aide de la théorie des systèmes dynamiques non linéaires." Toulouse, INSA, 1996. http://www.theses.fr/1996ISAT0031.
Повний текст джерелаKaakai, Fateh. "Modélisation et évalution des pôles d'échanges multimodaux : une approche hybride multiéchelle basée sur les réseaux Pétri Lots." Besançon, 2007. http://www.theses.fr/2007BESA2038.
Повний текст джерелаA Multimodal Hub is a complex transportation system which has the role to interconnect several public and private transportation modes in order to promote intermodality practice. Because of many observed problems (such as recurrent congestion phenomena inside stations, high transfer times, long queues in front of services, etc. ) which contribute to deteriorate the image of public transport in general, it becomes more and more important for transit authorities to be able to perform many performance measures for identifying the causes of these problems and trying to find solutions. The main goal of the PhD thesis is to propose a simulation model for evaluating the main performance factors of multimodal transportation hubs. Among the most important quantitative factors, we can mention occupancy rates, queue lengths, mean service times, evacuation times, and measures related to intermodality practice such as connection times and waiting times. The suggested simulation model is based on Batches Petri nets which are an extension of Hybrid Petri nets. This paradigm is suitable for our study because it offers a multiscale modular modeling approach which allows mastering the complexity of the studied system. Besides, it offers formal analysis techniques for checking and design (control) purposes. This simulation model can be successfully used for (i) evaluating existing multimodal hubs, (ii) validating design projects of new multimodal hubs, and (iii) assisting designers during sizing and planning procedures
Yu, Boyang. "High-quality recovery of garment models and sewing patterns from 3D clothed human data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD056.
Повний текст джерелаRecovering high-quality garment models from 3D clothed human shapes can enhance the interpretability of real garments and their digital reproduction, benefiting applications like VR and virtual try-ons. This thesis tackles the challenge of reconstructing garment geometry by estimating an animatable replica and its 2D pattern. Using a differentiable cloth simulator, we optimize the simulated garment to match the target shape while preserving key properties like symmetry. Our inverse garment design pipeline aligns with industry-standard modeling and simulation processes, adjusting 2D patterns and material properties to refine geometry and enable reanimation. Additionally, we introduce a deformation-based optimization method that refines mesh geometry to capture fine-grained details, improving fit and supporting non-rigid registration. Experiments on real and synthetic data demonstrate that our methods surpass state-of-the-art techniques in garment model quality and pattern accuracy
Jaberzadeh, Amir. "Simulation de transfert de chaleur et l'optimisation automatique des probes trajectoires multiple de la planification pré-opératoire pour les interventions percutanées thermique." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAD003.
Повний текст джерелаThere exist several minimally invasive techniques to perform tumor ablation procedures.Cryosurgery is one of these techniques and works by decompressing very rapidly the argon gas through a needle-like probe. It is hard for the surgeons to imagine final results and plan the surgery in advance in a complicated anatomical environment. Over-ablation or under ablation may result in complications during the treatment. So, due to a crucial need for having such a planning tool, in this thesis we focused on an automated pre-surgical planning for cryosurgery with goals to support the physician by utilizing a more realistic prediction of ablation zones and proposing a needle placement setup with a close to minimum risk to the patient and an optimal coverage of the tumor by the iceball in an acceptable time for the use in the operation room
Chajmowicz, Henri. "Modélisation et simulation numérique de structures articulées flexibles." Phd thesis, Marne-la-vallée, ENPC, 1996. http://pastel.archives-ouvertes.fr/pastel-00569038.
Повний текст джерелаCifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.
Повний текст джерелаThe key role that AI could play in improving business operations has been known for a long time, but the penetration process of this new technology has encountered certain obstacles within companies, in particular, implementation costs. On average, it takes 2.8 years from supplier selection to full deployment of a new solution. There are three fundamental points to consider when developing a new model. Misalignment of expectations, the need for understanding and explanation, and performance and reliability issues. In the case of models dealing with supply chain data, there are five additionally specific issues: - Managing uncertainty. Precision is not everything. Decision-makers are looking for a way to minimise the risk associated with each decision they have to make in the presence of uncertainty. Obtaining an exact forecast is a advantageous; obtaining a fairly accurate forecast and calculating its limits is realistic and appropriate. - Handling integer and positive data. Most items sold in retail cannot be sold in subunits. This simple aspect of selling, results in a constraint that must be satisfied by the result of any given method or model: the result must be a positive integer. - Observability. Customer demand cannot be measured directly, only sales can be recorded and used as a proxy. - Scarcity and parsimony. Sales are a discontinuous quantity. By recording sales by day, an entire year is condensed into just 365 points. What’s more, a large proportion of them will be zero. - Just-in-time optimisation. Forecasting is a key function, but it is only one element in a chain of processes supporting decision-making. Time is a precious resource that cannot be devoted entirely to a single function. The decision-making process and associated adaptations must therefore be carried out within a limited time frame, and in a sufficiently flexible manner to be able to be interrupted and restarted if necessary in order to incorporate unexpected events or necessary adjustments. This thesis fits into this context and is the result of the work carried out at the heart of Lokad, a Paris-based software company aiming to bridge the gap between technology and the supply chain. The doctoral research was funded by Lokad in collaborationwith the ANRT under a CIFRE contract. The proposed work aims to be a good compromise between new technologies and business expectations, addressing the various aspects presented above. We have started forecasting using the exponential smoothing family which are easy to implement and extremely fast to run. As they are widely used in the industry, they have already won the confidence of users. What’s more, they are easy to understand and explain to an unlettered audience. By exploiting more advanced AI techniques, some of the limitations of the models used can be overcome. Cross-learning proved to be a relevant approach for extrapolating useful information when the number of available data was very limited. Since the common Gaussian assumption is not suitable for discrete sales data, we proposed using a model associatedwith either a Poisson distribution or a Negative Binomial one, which better corresponds to the nature of the phenomena we are seeking to model and predict. We also proposed using Monte Carlo simulations to deal with uncertainty. A number of scenarios are generated, sampled and modelled using a distribution. From this distribution, confidence intervals of different and adapted sizes can be deduced. Using real company data, we compared our approach with state-of-the-art methods such as DeepAR model, DeepSSMs and N-Beats. We deduced a new model based on the Holt-Winter method. These models were implemented in Lokad’s work flow