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Nonlinear beam matching to gas-filled multipass cells
No full textInternational audienceGas-filled multipass cells are an appealing alternative to capillaries to implement nonlinear temporal compression of high energy femtosecond lasers. Here, we provide an analytic expression for stationary beam coupling to multipass cells that takes into account nonlinear propagation. This allows a constant beam size on the mirrors and at the cell waist, thereby making the optical design more accurate, for example to avoid optical damage or significant ionization. The analysis is validated using spatio-temporal numerical simulations of the propagation in a near-concentric configuration. This is particularly important for multipass cells that are operated in a highly nonlinear regime, which is the current trend since it allows a lower number of roundtrips, relaxing the constraint on mirror coatings performance
A three-pronged approach to predict the effect of plastic orthotropy on the formability of thin sheets subjected to dynamic biaxial stretching
No full textInternational audienceIn this paper, we have investigated the effect of material orthotropy on the formability of metallic sheets subjected to dynamic biaxial stretching. For that purpose, we have devised an original three-pronged methodology which includes a linear stability analysis, a nonlinear two-zone model and finite element calculations. We have studied 5 different materials whose mechanical behavior is described with an elastic isotropic, plastic anisotropic constitutive model with yielding based on Hill (1948) criterion. The linear stability analysis and the nonlinear two-zone model are extensions of the formulations developed by Zaera et al. (2015) and Jacques (2020), respectively, to consider Hill (1948) plasticity. The finite element calculations are performed with ABAQUS/Explicit (2016) using the unit-cell model developed by Rodríguez-Martínez et al. (2017), which includes a sinusoidal spatial imperfection to favor necking localization. The predictions of the stability analysis and the two-zone model are systematically compared against the finite element results – which are considered as the reference approach to validate the theoretical models – for loading paths ranging from plane strain stretching to equibiaxial stretching, and for different strain rates ranging from 100s−1 to 50000s−1. The stability analysis and the two-zone model yield the same overall trends obtained with the finite element simulations for the 5 materials investigated, and for most of the strain rates and loading paths the agreement for the necking strains is also quantitative. Notably, the differences between the finite element results and the two-zone model rarely go beyond 5%. Altogether, the results presented in this work provide new insights into the mechanisms which control dynamic formability of anisotropic metallic sheets
A Non-Nested Infilling Strategy for Multi-Fidelity based Efficient Global Optimization
No full textInternational audienceEfficient Global Optimization (EGO) has become a standard approach for the global optimization of complex systems with high computational costs. EGO uses a training set of objective function values computed at selected input points to construct a statistical surrogate model, with low evaluation cost, on which the optimization procedure is applied. The training set is sequentially enriched, selecting new points, according to a prescribed infilling strategy, in order to converge to the optimum of the original costly model. Multi-fidelity approaches combining evaluations of the quantity of interest at different fidelity levels have been recently introduced to reduce the computational cost of building a global surrogate model. However, the use of multi-fidelity approaches in the context of EGO is still a research topic. In this work, we propose a new effective infilling strategy for multi-fidelity EGO. Our infilling strategy has the particularity of relying on non-nested training sets, a characteristic that comes with several computational benefits. For the enrichment of the multi-fidelity training set, the strategy selects the next input point together with the fidelity level of the objective function evaluation. This characteristic is in contrast with previous nested approaches, which require estimation all lower fidelity levels and are more demanding to update the surrogate. The resulting EGO procedure achieves a significantly reduced computational cost, avoiding computations at useless fidelity levels whenever possible, but it is also more robust to low correlations between levels and noisy estimations. Analytical problems are used to test and illustrate the efficiency of the method. It is finally applied to the optimization of a fully nonlinear fluid-structure interaction system to demonstrate its feasibility on real large-scale problems, with fidelity levels mixing physical approximations in the constitutive models and discretization refinements
Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
No full textBuilding autonomous machines that can explore open-ended environments, discover possible interactions and autonomously build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by autonomous and intrinsically motivated learning agents that can generate, select and learn to solve their own problems. In recent years, we have seen a convergence of developmental approaches, and developmental robotics in particular, with deep reinforcement learning (RL) methods, forming the new domain of developmental machine learning. Within this new domain, we review here a set of methods where deep RL algorithms are trained to tackle the developmental robotics problem of the autonomous acquisition of open-ended repertoires of skills. Intrinsically motivated goal-conditioned RL algorithms train agents to learn to represent, generate and pursue their own goals. The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions, which results in new challenges compared to traditional RL algorithms designed to tackle pre-defined sets of goals using external reward signals. This paper proposes a typology of these methods at the intersection of deep RL and developmental approaches, surveys recent approaches and discusses future avenues
Concepts et sémantique des langages de programmation 2 : constructions modulaires et objet avec OCaml, Python, C++, Ada et Java
No full textNational audienceCet ouvrage explore les constructions syntaxiques des langages de programmation les plus courants, avec un éclairage mathématique sur leurs sémantiques et une présentation précise des aspects matériels qui interfèrent avec le codage.Ce deuxième volume présente un modèle sémantique original commun aux constructions et opérations des modules et des classes : visibilité, importation, exportation, définitions différées, paramétrisation par types et valeurs, extensions. Ce modèle fonde l’étude des modules d’Ada, OCaml et des fichiers d’en-tête de C. Il est décliné pour modéliser les traits objet puis utilisé pour traiter les classes de C++, Java, Python et OCaml.Concepts et sémantique des langages de programmation 2 s’adresse aux étudiants et enseignants des cursus informatiques ainsi qu’aux programmeurs chevronnés, qui y trouveront un guide de lecture des manuels de référence ainsi que les fondements de la vérification de programmes
Some Aminocyclopropane Chemistry and some Organotitanium Games with Alkynes
No full textInternational audienceBicyclic aminocyclopropane compounds constitute a special class of strained molecules which can undergo cyclopropane-ring opening under a variety of conditions [1,2]. During this talk, several examples will be presented, obtained in our group over the last few years [3-5]. The mechanisms of these reactions involve the generation of iminium, enamine or azomethine ylid species.In the second part of the talk, our most recent results on the reactions of terminal alkynes with low-valent titanium reagents will be presented [6-7].[1] V. A. Rassadin, Y. Six, Tetrahedron, 2016, 72, 4701-4757.[2] Y. Six in Targets in Heterocyclic Systems, O. A. Attanasi, P. Merino, D. Spinelli, Eds.; Società Chimica Italiana: Roma (2017); Vol. 21, pp. 277−307.[3] A. Wasilewska, B. A. Woźniak, G. Doridot, K. Piotrowska, N. Witkowska, P. Retailleau, Y. Six, Chem. Eur. J., 2013, 19, 11759-11767.[4] C. Chen, P. Kattanguru, O. A. Tomashenko, R. Karpowicz, G. Siemiaszko, A. Bhattacharya, V. Calasans, Y. Six, Org. Biomol. Chem., 2017, 15, 5364-5372.[5] A. Wolan, J. A. Kowalska-Six, H. Rajerison, M. Césario, M. Cordier, Y. Six, Tetrahedron, 2018, 74, 5248-5257 (“Barton Centennial Symposium in Print” special issue).[6] G. Siemiaszko, Y. Six, New J. Chem. 2018, 42, 20219–20226.[7] W. Frites, G. Siemiaszko, X. Ren, Y. Six, unpublished results
Navigation anomaly detection : An added value for Maritime Cyber Situational Awareness
No full textInternational audienceThe maritime sector is facing a continuous shift towards digitalization. A ship built during the last decade shows all characteristics of a comprehensive information system, combining information and operational technologies. While industrial programmable logic controllers are used for engine and power management, the bridge is now highly relying on digital sensors, networks and displays for navigation. Meanwhile, over the last few years, many cyber attacks targeting maritime assets and made publicly confirm a real interest of criminal and non-state actors in this critical sector for our globalized economies. In this work, a concept designed to detect and visualize advanced navigation cyber attacks on maritime systems using contextual NMEA data analytics is presented. A strategy is built to enhance the detection of navigation spoofing attacks, assess possible physical impacts onboard and support decision makers
Iterative Solution of Linear Matrix Inequalities for the Combined Control and Observer Design of Systems with Polytopic Parameter Uncertainty and Stochastic Noise
No full textInternational audienceMost research activities that utilize linear matrix inequality (LMI) techniques are based on the assumption that the separation principle of control and observer synthesis holds. This principle states that the combination of separately designed linear state feedback controllers and linear state observers, which are independently proven to be stable, results in overall stable system dynamics. However, even for linear systems, this property does not necessarily hold if polytopic parameter uncertainty and stochastic noise influence the system’s state and output equations. In this case, the control and observer design needs to be performed simultaneously to guarantee stabilization. However, the loss of the validity of the separation principle leads to nonlinear matrix inequalities instead of LMIs. For those nonlinear inequalities, the current paper proposes an iterative LMI solution procedure. If this algorithm produces a feasible solution, the resulting controller and observer gains ensure robust stability of the closed-loop control system for all possible parameter values. In addition, the proposed optimization criterion leads to a minimization of the sensitivity to stochastic noise so that the actual state trajectories converge as closely as possible to the desired operating point. The efficiency of the proposed solution approach is demonstrated by stabilizing the Zeeman catastrophe machine along the unstable branch of its bifurcation diagram. Additionally, an observer-based tracking control task is embedded into an iterative learning-type control framework
EZIOTracer: unifying kernel and user space I/O tracing for data-intensive applications
No full textInternational audienceTracing is a popular method for evaluating, investigating, and modeling the performance of today's storage systems. Tracing has become crucial with the increase in complexity of modern storage applications/systems, that are manipulating an ever-increasing amount of data and are subject to extreme performance requirements. There exists many tracing tools focusing either on the user-level or the kernel-level, however we observe the lack of a unified tracer targeting both levels: this prevents a comprehensive understanding of modern applications' storage performance profiles. In this paper, we present EZIOTracer, a unified I/O tracer for both (Linux) kernel and user spaces, targeting data intensive applications. EZIOTracer is composed of a userland as well as a kernel space tracer, complemented with a trace analysis framework able to merge the output of the two tracers, and in particular to relate user-level events to kernel-level ones, and vice-versa. On the kernel side, EZIOTracer relies on eBPF to offer safe, low-overhead, low memory footprint, and flexible tracing capabilities. We demonstrate using FIO benchmark the ability of EZIOTracer to track down I/O performance issues by relating events recorded at both the kernel and user levels. We show that this can be achieved with a relatively low overhead that ranges from 2% to 26% depending on the I/O intensity