Minvydas Ragulskis

Professor at KTU Faculty of Mathematics and Natural Sciences

Minvydas Ragulskis is a professor at KTU Faculty of Mathematics and Natural Sciences. In 2005 M. Ragulskis was awarded the Lithuanian Science Prize. The scientist is a member of the Lithuanian Academy of Sciences, an invited expert of the European Commission, a member of the Belgian Science Council’s mathematical sciences panel.

In addition, he is a member of the editorial boards of several international scientific journals, a member of the organizing committees of many international conferences. The main research direction of M. Ragulskis is the dynamics and applications of nonlinear systems, in which machine learning, evolutionary optimization, and the application of artificial intelligence play an important role.

  • Dynamics and applications of nonlinear systems
  • Machine learning

A Novel AI-Based Automated Identification of Cracks in Concrete Bridges and Offshore Oil Installations (ConcreteAI)

Environmental factors affecting concrete structures like bridges, beams, columns and highways in onshore and offshore environments lead to development of micro-cracks. Early detection of surface micro-cracks in concrete structures helps to put preventive measures in place to avoid failure potentially saving loss of assets and in some cases lives.

In this project scientists created an automated AI based system which will allow training and testing of real time images of concrete bridges and offshore structures, which are augmented by the presence of shadows and other noises. Researchers have worked hard on the development of an image database of images of concrete structures with cracks and shadows, which will then be used for training and testing of the AI network created specifically for this project.

Solitary solutions to stochastic COVID-19 pandemic models

The goal of the project is to find soliton solutions in stochastic models of the COVID-19 pandemic and to propose pandemic management methodologies based on the topological properties of these solutions. During the project, scientis improved the model of the COVID-19 pandemic by introducing diffuse and multiplicative relationships and adapting the model to the dynamics of Lithuanian diseases. The have also achieved important mathematical results which allowed a new look at the processes of the spread and control of pandemics.

Key Technologies of bridge health monitoring based on integration of big data and artificial intelligence

The project was carried out with Hohai University in China. The created algorithms for the identification of chaotic systems are applied to the diagnosis of defects in complex structures. The project is important for the safe operation of engineering structures. The results are patented in China.

Improving the applicability of nature-inspired optimization by bridging theory and practice

2016-2020 project. It was focused on the development and application of optimization algorithms inspired by nature. The results are applicable in many fields of science and technology. Project partners: Belgium, France, North Macedonia, Italy, Denmark, Germany, Ireland, Romania, Serbia, Slovakia, Slovenia, United Kingdom, Austria, Bosnia and Herzegovina, Czech Republic, Greece, Israel, Norway, Poland, Portugal, Spain, Lithuania.

Algebraic Techniques and Algorithms for Time Series Analysis (ALETMA

This project is dedicated for the development of new methods and algorithms for time series analysis and application of these algorithms for the identification, forecasting and segmentation of chaotic processes. The generalization of the algebraic skeleton in 2D enabled to design novel segmentation algorithms for complex intertwined sequences and digital images. The results of the project are applied in cardiology.

Nr. Publikacijos pavadinimas Autoriai Metai
1. Pinning impulsive synchronization of stochastic delayed neural networks via uniformly stable function Lijun PanQiang SongJinde CaoMinvydas Ragulskis 2022
2. An overview of challenges associated with automatic detection of concrete cracks in the presence of shadows Mayur Pal, Paulius Palevičius, Mantas Landauskas, Ugnė Orinaitė, Inga Timofejeva, Minvydas Ragulskis 2021
3. Recent Trends in Chaotic, Nonlinear and Complex Dynamics Jan Awrejcewicz, Rajasekar Shanmuganathan, Minvydas Ragulskis 2021
4. A data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: verification on a structural health monitoring benchmark structure Tongwei Liu, Hao Xu, Minvydas Ragulskis, Maosen Cao, Wiesław Ostachowicz 2020
5. Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts Xia Zhou, Jun Cheng, Jinde Cao, Minvydas Ragulskis 2020