Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl

Methods of optimization of technological processes of restoration of steel coverings

Методи оптимізації технологічних процесів відновлення сталевих покриттів = Methods of optimization of technological processes of restoration of steel coverings / Т. В. Смірнова, О. М. Дрєєв, О. А. Смірнов, Є. К. Солових // Shipbuilding & Marine Infrastructure. – 2019. – № 1 (11). – C. 48–57.Анотація. Мета. Метою роботи є дослідження методів оптимізації технологічних процесів відновлення сталевих покриттів. Методика. Для досягнення поставленої мети необхідно вирішити такі завдання: 1) сформулювати технологічний процес відновлення поверхонь сталевих валів як ланцюг технологічних операцій; 2) дослідити методи оптимізації технологічних процесів; 3) розглянути проблему комбінаторної складності структурної оптимізації технологічного процесу; 4) визначити підходи до формування інтелектуальної системи оптимізації технологічного процесу відновлення сталевих покриттів. Результати. Визначено, що точне розв’язання задачі оптимізації у вигляді повного перебору всіх можливих ланцюгів технологічного процесу є нереальною задачею з причини високої обчислювальної складності.Abstract. Aim. The purpose of this work is to investigate methods of optimization of technological processes of steel coating restoration. Methodology. To achieve this purpose, it is necessary to solve the following problems: 1. To formulate the technological process of restoring the surfaces of steel shafts as a chain of technological operations. 2. Investigate methods of technological process optimization. 3. To consider the problem of combinatorial complexity of structural optimization of technological process. 4. Identify approaches to the formation of an intelligent system for optimization of technological process of steel coatings restoration. Results. 1. It is determined that the exact solution of the optimization problem, in the form of a complete search of all possible chains of the process, is an unrealistic task because of the high computational complexity. 2. It is determined that in order to solve the optimization problem on the polymorphic chain of technological operations, the information intelligent system of process optimization should use the unification of the technological operation. 3. It is determined that an intelligent information system should support a set of optimization algorithms on a unified set of technological operations. 4. The neural network contained in the intelligent information system must learn from the solutions obtained in other ways. 5. The study showed the lack of development of existing information systems that can be used in cloud computing to optimize the chain of technological operations for the recovery of steel shaft surfaces. Therefore, developing your own information system that meets these requirements is an urgent task that needs to be addressed further. Scientific novelty. The study revealed a contradiction between existing approaches to optimizing the technological processes of repairing steel coatings and current requirements for information systems that can be used in cloud computing to optimize the chain of technological operations of repairing surfaces of steel shafts. The scientific task, development of own information system which satisfies the specified requirements is formulated. Practical significance. Using the proposed approach will increase the hardness, durability, improve the porosity parameters, reduce the roughness in the restoration of steel coatings