Scientific and Educational Laboratory of Big Data Analysis Methods

There are many problems in physics, biology and other natural sciences in which symbolic regression can provide valuable information and discover new laws of nature. The widespread deep neural network does not offer interpretable solutions. Meanwhile, symbolic expressions indicate a clear connection between the observations and the target variable. However, at the moment there is no dominant solution for the symbolic regression problem, and we are striving to reduce this gap with our project. Our laboratory has started research in this direction, and our approach to finding a representation of the symbolic law involves the use of generative models along with optimization methods with constraints. It can be applied to equations in closed form or to a system of differentiable equations. The objective of the study is to improve the model by using active/zero learning methods.

High-precision modeling of installations and systems is one of the main directions of industrial data analysis today. Models of systems, their digital counterparts, are used to predict their behavior under various conditions. We have developed a digital twin of a data storage system (DSS) using generative machine learning models. The system consists of several types of components: HDD and SSD disks, disk pools with different RAID arrays, cache and storage controllers. Each storage component is represented by a probabilistic model that describes the probability distribution of the values of the component performance parameters depending on their configuration and the parameters of the external data load. Using machine learning allows you to get a high-precision digital twin of a specific system, spending less time and resources than other analogues. It allows you to quickly predict the performance of the system and its components under different configurations and external data loads, which significantly speeds up the development of new storage systems. Also, comparing the forecasts of the double with the indicators of the real storage system allows you to diagnose failures and anomalies in the system, increasing its reliability.

Forecasting and checking the state of the weather is the task of extrapolating a number of indicators. Modern weather research and forecasting models work well under well-known conditions and short time intervals. On the other hand, it is known that AI methods, available data, and weather simulators do not perfectly match each other. Thus, this project is aimed at developing and training new algorithms to adjust the parameters of the simulator and more effectively obtain reliable forecasts. This synergy, in turn, will improve the accuracy of forecasts of normal and abnormal weather conditions for a longer period.

The development of new materials with the properties of electric energy storage is the most important task of the modern energy industry. Two-dimensional crystals based on the principles of graphene lattices can be used to produce such materials. The search for crystal lattice configurations is complicated by the multitude of possible options and the length of the test cycle for a single configuration. Many resource-intensive in silico and in vitro tests are required. These algorithms are aimed at realizing the possibility of predicting the energy properties of crystals of a given configuration and solving the problem of inference - determining the optimal crystal configuration according to a given energy characteristic. Combining such algorithms will significantly reduce the time for searching and synthesizing practically useful energy carriers.