Approximation of the critical buckling factor for composite panels

This article is concerned with the approximation of the critical buckling factor for thin composite plates. A new method to improve the approximation of this critical factor is applied based on its behavior with respect to lamination parameters and loading conditions. This method allows accurate approximation of the critical buckling factor for non-orthotropic laminates under complex combined loadings (including shear loading). The influence of the stacking sequence and loading conditions is extensively studied as well as properties of the critical buckling factor behavior (e.g concavity over tensor D or out-of-plane lamination parameters). Moreover, the critical buckling factor is numerically shown to be piecewise linear for orthotropic laminates under combined loading whenever shear remains low and it is also shown to be piecewise continuous in the general case. Based on the numerically observed behavior, a new scheme for the approximation is applied that separates each buckling mode and builds linear, polynomial or rational regressions for each mode. Results of this approach and applications to structural optimization are presented

Relativity and Accelerator Engineering

From a geometrical viewpoint, according to the theory of relativity, space and time constitute a four-dimensional continuum with pseudo-Euclidean structure. This has recently begun to be a practically important statement in accelerator physics. An X-ray Free Electron Laser (XFEL) is in fact the best, exciting example of an engineering system where improvements in accelerator technology makes it possible to develop ultrarelativistic macroscopic objects with an internal fine structure, and the theory of relativity plays an essential role in their description. An ultrarelativistic electron bunch modulated at nanometer-scale in XFELs has indeed a macroscopic finite-size of order of 10 $\mu$m. Its internal, collective structure is characterized in terms of a wave number vector. Here we will show that a four-dimensional geometrical approach, unusual in accelerator physics, is needed to solve problems involving the emission of radiation from an ultrarelativistic modulated electron beam accelerating along a curved trajectory. We will see that relativistic kinematics enters XFEL physics in a most fundamental way through the so-called Wigner rotation of the modulation wave number vector, which is closely associated to the relativity of simultaneity. If not taken into account, relativistic kinematics effects would lead to a strong qualitative disagreement between theory and experiments. In this paper, several examples of relativistic kinematics effects, which are important for current and future XFEL operation, are studied. The theory of relativity is applied by providing details of the clock synchronization procedure within the laboratory frame. This approach, exploited here but unusual in literature, is rather "practical", and should be acceptable to accelerator physicists

Safe Reinforcement Learning as Wasserstein Variational Inference: Formal Methods for Interpretability

Reinforcement Learning or optimal control can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and corresponding optimal policy. Consequently, formalizing the sequential decision-making problems as inference has a considerable value, as probabilistic inference in principle offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of the reward design and policy convergence. In this study, we propose a novel Adaptive Wasserstein Variational Optimization (AWaVO) to tackle these challenges in sequential decision-making. Our approach utilizes formal methods to provide interpretations of reward design, transparency of training convergence, and probabilistic interpretation of sequential decisions. To demonstrate practicality, we show convergent training with guaranteed global convergence rates not only in simulation but also in real robot tasks, and empirically verify a reasonable tradeoff between high performance and conservative interpretability.Comment: 24 pages, 8 figures, containing Appendi

Optimization of tau identification in ATLAS experiment using multivariate tools

In elementary particle physics the efficient analysis of huge amount of collected data require the use of sophisticated selection and analysis algorithms. We have implemented a Support Vector Machine (SVM) integrated with the CERN TMVA/ROOT package. SVM approach to signal and background separation is based on building a separating hyperplane defined by the support vectors. The margin between them and the hyperplane is maximized. The extensions to a non-linear separation is performed by mapping the input vectors into a high dimensional space, in which data can be linearly separated. The use of kernel functions allows to perform computations in a high dimension feature space without explicitly knowing a mapping function. Our SVM implementation is based on Platt's Sequential Minimal Optimization (SMO) algorithm and includes various kernel functions like a linear function, polynomial and Gaussian. The identification of hadronic decays of tau leptons in the ATLAS experiment using a tau1P3P package is performed using, beside the baseline cut analysis, also multivariate analysis tools: neural network, PDE_RS and our implementation of the SVM algorithm. The use and the comparison of the three algorithms is presented

The population tracking model:a simple, scalable statistical model for neural population data

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca[Formula: see text] and voltage imaging tools