Welcome to the regular seminars on current research topics in computational mechanics!

Presentations are given by invited lecturers from Skoltech as well as from outside to introduce students to current trends and advances in diverse areas of modern fluid and solid mechanics, applied mathematics, computational science, and industrial applications of mechanics. Students have the opportunity to learn from and interact with leading experts in computational mechanics and to enjoy exposure to cutting-edge topics and open problems in the field.

Speaker's report: 50 min.

Q&A: 10-15 min.

Seminars are held in English.

Lead Instructor: Aslan Kasimov, Associate Professor

Contacts: A.Kasimov@skoltech.ru

APRIL 2, 2:00 PM | NEW CATALYSTS FOR SUSTAINABLE DEVELOPMENT AND DECARBONIZATION

Location: R3-2009
Speaker: Aleksandra Radina, PhD student of Materials Science, Member of the Industry-Oriented Computational Discovery Group, Skoltech 

Nowadays the most widely used catalysts for organic synthesis are mainly made of noble and rare earth metals which significantly increases the cost of many products. Moreover, 80% of catalyst used in domestic industry including oil and chemical production are imported, and it can cause problems in the present political situation. To solve this problem, the Industry-Oriented Computational Discovery Group at Skoltech is currently conducting studies to find a new catalyst that would be as effective as those made from precious metals, but cheaper. Transition metal carbides and borides have been suggested as promising candidates for new catalysts. Recently, some of our theoretical predictions have been confirmed experimentally. It has been shown that higher transition metal borides have great catalytic activity for the conversion of CO2 into various fuels and hydrogen production processes.

APRIL 9, 2:00 PM | INVESTIGATION OF THE MECHANISMS OF FORMATION OF SPATIOTEMPORAL STRUCTURES ARISING AT THE PROPAGATING REACTION FRONT

Location: R3-2009
Speaker: Eduard Yakupov, Junior research scientist, Laboratory for Nonlinear Dynamics and Theoretical Biophysics, P.N.Lebedev Physical Institute of the Russian Academy of Sciences

In experimental studies of combustion wave propagation in gaseous media, it has been found that under certain conditions autowaves - spirals or targets - patterns appear at the wave front. This study aims to solve pressing problems in nonlinear dynamics by examining autowave and dissipative structures emerging at a propagating reaction front. It explores the formation and evolution of complex spatiotemporal structures in distributed dynamic systems, focusing on processes occurring at the reaction front. The research employs an approach that considers the hierarchical nature of spatial-temporal self-organization via block models. Key outcomes include developing a novel methodology for analyzing the mechanisms underlying these structures' formation, detailing conditions necessary for the emergence of autowave and Turing structures, creating a reduced model for high-pressure hydrogen-air combustion, and establishing quantitative criteria for the formation of different structure types based on system parameters.

APRIL 16, 2:00 PM | THE PROBLEM OF FITTING AI MODELS TO EMPIRICAL DATA IN HYBRID MODELLING

Location: R3-2009
Speaker: Ivan Tyukin, Professor, Artificial Intelligence Center, Skoltech

Recent years have seen drastic increase in the popularity of advance machine learning & AI tools such as neural networks in various modelling settings. Nowadays classical solvers and approaches often go hand-in-hand with their data-driven counterparts complementing each other as appropriate. Relevant use-cases include physics-informed neural nets as replacements for solvers of large, complicated problems or modelling complex empirical relationships in otherwise classical models by neural nets. In these and many other similar cases, one of the pressing questions is how much data we need to fit our model to data and what guarantees can be made for a given model and dataset at hand. In this talk we will delve into the problem of fitting models to data with the view to develop an understanding of basic mathematical ingredients needed to answer these questions. We will look at the problem from two alternatives directions: statistical learning theory and approximation theory. We will discuss similarities and discrepancies between these two approaches. We will also present a recent example of the application of machine learning for extrapolating solutions of integro-differential equations. The seminar is intended for a broad audience of PhD students and researchers interested in developing a normative and rigorous take on the application of neural networks in modelling.