pdf/deep-learning-based-reconstruction-of-the-neutrino-direction-1h3x47to.pdf

Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. Published by the American Physical Society 2024 

Bayesian optimization algorithms for accelerator physics

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program. 

Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics

Introduction of the technology of AE monitoring at OPP

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. 

Bayesian Optimization Algorithms for Accelerator Physics

Toward the end-to-end optimization of particle physics instruments with differentiable programming

Integration of АЕ method into the technology of repair and extension of life of metal structures

the paper proposes moving from spatial problems to problems on the plane in similar problems. This will significantly reduce the machine memory requirements and shorten the calculation time.

Reduction of Numerical Arrays in Magnetometry Problems Calculations

/pdf/evaluation-of-the-state-and-prediction-of-breaking-load-at-28njr5eikt.pdf

Evaluation of the state and prediction of breaking load at acoustic-emission testing of pressure vessels with limited access to controlled surface

The main task in designing and modernization of a modern grain processing and storage enterprise is a thorough study of possibilities and deep automation at the modern level. Automation of technological processes on the basis of modern technology should ensure intensification of production, increase in quality and decrease in cost of production. This need follows from the production activity of the elevator analysis. Despite the fact that the introduction of modern equipment, tools, means of mechanization and automation of individual operations, improvement of work organization as a whole ensured the fulfillment of these tasks, there are still significant reserves for increasing labor productivity and improving its technical and economic indicators. These reserves consist, first of all, in the optimization and automation of operational management of processes and in the improvement of work organization. The article is devoted to increasing the loading noria efficiency due to the use of ladles without a bottom as well as a simple asynchronous motor controlling system - controlling by changing voltage on the stator.

Increasing of the Elevator Noria Efficiency

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