The group’s main research areas include numerical optimization methods, geometric modeling, evolutionary algorithms, and computational fluid dynamics (CFD). Special emphasis is placed on the application and development of artificial intelligence models for dana analysis, system behavior prediction, and support of the engineering design process. By integrating computational mechanics, optimization techniques, and artificial intelligence, the group develops innovative solutions to improve the efficiency, performance, and sustainability of engineering processes and products.

• Numerical optimization methods
• Geometric modeling
• Evolutionary algorithms
• Computational fluid dynamics (CFD)
• Application of existing and development of novel artificial intelligence models

Laboratories

• Laboratory for Optimization and Design
• Rapid Prototyping Laboratory
• Ad hoc Wind Tunnel for VAWT Analysis
• CFD Analysis Laboratory
• Laboratory for Numerical Modeling and Optimization
• Laboratory for Fluid Mechanics and Hydraulic Systems
• Computational Fluid Dynamics Laboratory

Equipment

• High-accuracy and high-precision 3D scanners (GOM ATOS I and GOM ATOS COMPACT 8), Prusa and Modix 120X 3D printers, Proxxon FF 500 CNC Micro Mill, High-performance workstations and graphics stations
• Hydraulic machinery system (circulation pumps, fans, filters, etc.), Various measuring instruments (pressure measurement, velocity measurement, hot-wire anemometry, etc.)

Advanced computing resources

• Intel Xeon server (2 × 14 cores, 256 GB RAM)
• Intel Xeon server (2 × 16 cores, 260 GB RAM) equipped with an Nvidia A100 GPU
• Numerical optimization computing cluster with approximately 20 computers (Intel Core i7, 8 cores, 32 GB RAM each)

• HRZZ 2013: Adaptive Parameterization of Variable 3D Geometries for Shape Optimization and Meshless Numerical Modeling
• HRZZ 2018: Evolutionary Shape Synthesis with Integral and Partitioned Phenotypes, Dynamic Parameterization, and Meshless Modeling
• HRZZ 2022: Numerical Synthesis of 3D Shapes Using Advanced Geometry Parameterization, Experimental Validation, and Feedback Adaptation of Numerical Models
• HRZZ 2025: Estimation of Blood Glucose Levels in Individuals with Type 1 Diabetes from Facial Photographs and 3D Facial Scans Using Machine Learning

Damir Vučina: Head of the Research Group.

 

Milan Ćurković: Collaboration opportunities include geometric modeling, shape optimization, 2D and 3D data processing, computer vision, and machine learning. Particular emphasis is placed on developing methods for 3D reconstruction, geometric analysis, and system condition assessment based on image and geometric data, with applications in medicine, industry, and cultural heritage.

 

Ivo Marinić-Kragić: Research interests include numerical analysis and engineering optimization in computational fluid dynamics (CFD), structural static and dynamic analysis, thermal analysis, energy management in microgrid systems (photovoltaic, battery, and hybrid systems), and engineering applications of machine learning.