Dalia Čalnerytė

Associate Professor at KTU Faculty of Informatics

Dalia Čalnerytė is an associate professor at KTU Faculty of Informatics and a researcher of the research “Multidisciplinary models” group. D. Čalnerytė obtained a doctoral degree in computer science in the field of physical sciences in 2017. The researcher’s research areas are numerical system models, intelligent decision-making algorithms, optimization, and multidisciplinary models.

In ongoing scientific research and experimental development projects, D. Čalnerytė improves and applies artificial intelligence methods (image analysis, time series analysis, and clustering, optimization, machine learning), participated in several institutional research and R&D projects that applied and improved image analysis, time series analysis, and clustering, optimization, machine learning algorithms.

  • Numerical models of systems
  • Intelligent decision-making algorithms, optimization
  • Multidisciplinary models

Combination of Image Decomposition and Artificial Intelligence to Identify Evolution of Process (VDDISTPEN)

The researcher and other members of the team solved the problem of big data decomposition, which allows achieving better results compared to classical algorithms when the evolution of the process is evaluated by summarizing the results of discrete image sequence analysis.

Numerical Models of Short-Wave Physical Behaviour in Micro and Nano Structures (FEMSHORTWAVE)

The researcher led this project. Researchers solved the problem of time and resource cost optimization. It was solved using numerical modeling tasks. By combining the results obtained at different scales (larger and smaller), the researchers modeled the physical behavior of short waves for practically acceptable computational time and memory costs.

Development of prototype of innovative champignon robotics technology

The successful development of the mushroom segment is related to automatization solutions, and so the Company’s subsidiary Baltic Champs, UAB with an external partner implemented the project “Development of prototype of innovative champignon robotics technology” co-financed by EU structural funds. The Company is using artificial intelligence and robotic systems for research and technological development to reduce the risk of infections and diseases in mushrooms, and to increase the yield and quality of the mushrooms.

Schedule Planning Algorithm for Creating the Work Schedule of Aircraft Service Personnel

An algorithm based on evolutionary algorithms is developed and further developed for staff scheduling, given a list of tasks and a set of constraints describing work rules (applied in the airport sphere). During the creation of the schedule, employee shifts are formed dynamically depending on the set of tasks, maintaining the relationship between the convenience of the work schedule for employees and the optimization of working time.

Design of Machine Learning Based Algorithm for Personnel Scheduling (APTS)

Reinforcement-based algorithm to solve personnel rostering problem was created in this project. Numerical experiments were carried out for the benchmark dataset instances published in schedulingbenchmarks.org/, which describe roster requirements (shift demand and constraints) and are popular in scientific research. Although the best-known solutions have not been obtained using this algorithm, the idea to transfer the information from previously generated schedules with different number of employees and problem horizon was confirmed. The novelty of the project is that the created algorithm enables to generate a schedule under consideration of the experience accumulated while generating other schedules under the analogous set of constraints.

Combination of Image Decomposition and Artificial Intelligence to Identify Evolution of Process (VDDISTPEN)

During the project, researchers developed a methodology determining the similarity and development of municipalities based on economic and social indicators, the results of satellite photo analysis. The obtained results can be used during political or economic decision-making, applying the good practices of municipalities to similar communities, etc.

Adapting an AI-based trip generation algorithm to work in real-world conditions

The project is under development. Researchers develop a trip generation algorithm combining geographical, price and consumer behavior data by applying stimulatory learning. The results are relevant for those who want to receive individual travel offers.

No. Title Authors Year
1. Development of a market trend evaluation system for policy making  Valentas Gružauskas, Andrius Kriščiūnas, Dalia Čalnerytė, Valentinas Navickas, Eva Koisova 2020
2. Synthesis of Highly Convergent 2D and 3D Finite Elements for Short Acoustic Wave Simulation  Dalia Čalnerytė, Andrius Kriščiūnas, Rimantas Barauskas 2020
3.  Finite element analysis of resonant properties of  silicon nanowires  Dalia Čalnerytė, Vidmantas Rimavičius, Rimantas Barauskas 2019
4. Multi-scale finite element modeling of 3D printed structures subjected to mechanical loads Dalia Čalnerytė, Rimantas Barauskas, Daiva Milašienė, Rytis Maskeliūnas, Audrius Nečiūnas, Armantas Ostreika, Martynas Patašius, Andrius Kriščiūnas 2018
5. Multi-scale evaluation of the linear elastic and failure parameters of the unidirectional laminated textiles  with application to transverse impact simulation  Dalia Čalnerytė, Rimantas Barauskas 2016