Current projects

    • "The optimization and statistical models and methods in recognizing properties of data sets measured with errors- Young Researchers' Career Development Project - Training of Doctoral Students", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Mirta Benšić

      Summary:The PhD student will deal with the methods of nonlinear regression and classification. Emphasis is placed on understanding, developing and applying nonparametric methods including neural networks. The specific purpose of this PhD education programme is to contribute to mathematical understanding of statistical and algorithmic properties of multilayer neural networks and related methods with a tendency to find expressions for the approximation of errors, complexity, statistical risk and time of calculation. Theoretically the results will be supported by simulations and applied to real problems. The PhD student is planned to enrol in the Joint Postgraduate Doctoral Study Programme in Mathematics of the universities of Osijek, Rijeka, Split and Zagreb, to specialise in the field of probability and mathematical ststistics and to further educate and train at the University of Yale (led by Prof. Andrew Barron).

      Programme: Young Researchers' Career Development Project - Training of Doctoral Students

      Mentor's name and surname: Mirta Benšić

      PhD name and surname: Una Radojičić

      Project duration: 1 March 2017 - 28 February 2021

    • "The optimization and statistical models and methods in recognizing properties of data sets measured with errors- Young Researchers' Career Development Project - Training of Doctoral Students", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Mirta Benšić

      Summary:The PhD student will deal with the methods of nonlinear regression and classification. Emphasis is placed on understanding, developing and applying nonparametric methods including neural networks. The specific purpose of this PhD education programme is to contribute to mathematical understanding of statistical and algorithmic properties of multilayer neural networks and related methods with a tendency to find expressions for the approximation of errors, complexity, statistical risk and time of calculation. Theoretically the results will be supported by simulations and applied to real problems. The PhD student is planned to enrol in the Joint Postgraduate Doctoral Study Programme in Mathematics of the universities of Osijek, Rijeka, Split and Zagreb, to specialise in the field of probability and mathematical ststistics and to further educate and train at the University of Yale (led by Prof. Andrew Barron).

      Programme: Young Researchers' Career Development Project - Training of Doctoral Students

      Mentor's name and surname: Mirta Benšić

      PhD name and surname: Una Radojičić

      Project duration: 1 March 2017 - 28 February 2021

    • "The optimization and statistical models and methods in recognizing properties of data sets measured with errors- Young Researchers' Career Development Project - Training of Doctoral Students", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Mirta Benšić

      Summary:The PhD student will deal with the methods of nonlinear regression and classification. Emphasis is placed on understanding, developing and applying nonparametric methods including neural networks. The specific purpose of this PhD education programme is to contribute to mathematical understanding of statistical and algorithmic properties of multilayer neural networks and related methods with a tendency to find expressions for the approximation of errors, complexity, statistical risk and time of calculation. Theoretically the results will be supported by simulations and applied to real problems. The PhD student is planned to enrol in the Joint Postgraduate Doctoral Study Programme in Mathematics of the universities of Osijek, Rijeka, Split and Zagreb, to specialise in the field of probability and mathematical ststistics and to further educate and train at the University of Yale (led by Prof. Andrew Barron).

      Programme: Young Researchers' Career Development Project - Training of Doctoral Students

      Mentor's name and surname: Mirta Benšić

      PhD name and surname: Una Radojičić

      Project duration: 1 March 2017 - 28 February 2021

    • "The optimization and statistical models and methods in recognizing properties of data sets measured with errors- Young Researchers' Career Development Project - Training of Doctoral Students", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Mirta Benšić

      Summary:The PhD student will deal with the methods of nonlinear regression and classification. Emphasis is placed on understanding, developing and applying nonparametric methods including neural networks. The specific purpose of this PhD education programme is to contribute to mathematical understanding of statistical and algorithmic properties of multilayer neural networks and related methods with a tendency to find expressions for the approximation of errors, complexity, statistical risk and time of calculation. Theoretically the results will be supported by simulations and applied to real problems. The PhD student is planned to enrol in the Joint Postgraduate Doctoral Study Programme in Mathematics of the universities of Osijek, Rijeka, Split and Zagreb, to specialise in the field of probability and mathematical ststistics and to further educate and train at the University of Yale (led by Prof. Andrew Barron).

      Programme: Young Researchers' Career Development Project - Training of Doctoral Students

      Mentor's name and surname: Mirta Benšić

      PhD name and surname: Una Radojičić

      Project duration: 1 March 2017 - 28 February 2021

    • "Optimization of parameter dependent mechanical systems - Young Researchers' Career Development Project - Training of Doctoral Students", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Ninoslav Truhar

      Summary: The doctoral student will deal with the optimization of active and passive damping of mechanical systems with and without external force. For this purpose, it will be necessary to develop a general theoretical framework that describes many important system properties and to construct adequate numerical algorithms for calculating the desired sizes. The doctoral candidate is planned to enroll in the Joint university postgraduate doctoral study program in mathematics at the universities of Osijek,Rijeka, Split and Zagreb and specialize in the field of control and optimization theory, i.e. ordinary differential equations and dynamic systems.

      Programme: Young Researchers' Career Development Project - Training of Doctoral Students

      Mentor's name and surname: Ninoslav Truhar

      PhD name and surname: Matea Puvača

      Project duration: 20 September 2016 - 20 September 2020

    • "Real-time measurements and forecasting for successful prevention and management of seasonal allergies in Croatia-Serbia cross-border region", (Department of Mathematics, J. J. Strossmayer University of Osijek - Interreg IPA CBC Croatia - Serbia 2014-2020) - Project coordinator: Kristian Sabo

      Summary: Allergen avoidance is important for managing allergy. Knowledge about when certain pollen types are likely to be in the air helps allergy sufferers to plan activities and medication use. Since airborne pollen is transported by air masses it can easily cross the border resulting an increased risk for allergy symptoms in sensitive population. Airborne allergens are routinely monitored in cross-border area. However, applied methodology is time consuming and results are disseminated to end users with a delay which limits the impact of collected data in every day health management. The project will modernize public health service and notably enhance the quality and applicative value of the information they provide in cross-border area: by introducing real time monitoring of airborne allergens, by developing models for prediction exposure and by creating a joint platform for instantaneous dissemination of these information. In addition the project will make an effort to educate end users on the benefits from using information for prevention and management of allergy symptoms coming from the information public health services will provide following the implementation of this project. The project will focus on three major pollen allergens (i.e. Birch, Grass, Ambrosia) and thus, having in mind overall prevalence of seasonal allergies in the Croatia-Serbia cross-border region, its results will enhance public health services needed for 15-30% of the population. Particular attention will be given to introduction of developed services to vulnerable groups i.e. children and elderly people for which it can help to plan travelling, outdoor activities, start of the therapy, self assessment of the therapy effectiveness etc. Joint approach for dissemination of measurements and forecasts will improve information flow for people travelling from one side of the border to another but also for visitors coming from other regions.

      Programme: Interreg IPA Cross-border Cooperation Programme Croatia - Serbia 2014-2020

      Project partners: Institut BioSens - Istraživačko razvojni institut za informacione tehnologije biosistema (Lead Beneficiary), Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu and Grad Osijek

      Project members: Kristian Sabo, Krešimir Burazin, Nenad Šuvak and Slobodan Jelić

      Project duration: 15 July 2017 - 14 January 2020

    • "Robustness optimization of damped mechanical systems", (Department of Mathematics, J. J. Strossmayer University of Osijek - Ministry of Science and Education and Deutscher Akademischer Austanschdienst (DAAD)) - Project coordinator: Zoran Tomljanović

      Summary: Mechanical systems have been widely investigated, but there are still many interesting and important open problems from the theoretical point of view and also from the applications themselves. Within this project we plan to consider robust damping optimization. The criterion for damping optimization that we want to consider corresponds to the H-infinity system norm which, compared to other criteria such as the H-2 norm or the total average energy, provides better damping properties in terms of the system's robustness. Thus, we plan to derive a new approach for efficient damping optimization and compare it to existing strategies.

      Programme: The programme aimed at encouraging the exchange of project participants between the Ministry of Science and Education of the Republic of Croatia and the DAAD

      Project partners: Technische Universität Berlin (Matthias Voigt, Volker Mehrmann and Philipp Schulze)

      Team members (UNIOS): Ninoslav Truhar and Matea Puvača

      Project duration: 1 January 2017 - 31 December 2018

    • "The optimization and statistical models and methods in recognizing properties of data sets measured with errors", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Rudolf Scitovski

      Summary: As a part of an attractive and active area of research known as big data analysis, optimization and statistical aspects of recognizing data sets properties will be analyzed. Research will be focused on clustering problems, deconvolution models and applications. The assumption is that the observed data sets represent the measured values of the variables to be analyzed but also that they contain a measurement error. In large data sets it is often appropriate to cluster data sets on the basis of certain characteristics and then apply models for each group that can describe variable properties such as relationship among them, possibility of separation, edges, specific form of the set of values, dimensions (length, surface or volume) of the set of values or general parameter vector which determines them. The problem in many practical situations can be formulated as an optimization problem for which the objective functions is generally neither differentiable nor convex. In order to solve such problems effectively, rapid and accurate numerical procedures will be developed. Also, due to errors in the data,in order to understand and correctly interpret the results, statistical models will be used and important statistical properties will be characterized.

      Programme: Croatian Science Foundation (IP-06-2016)

      Team members (UNIOS): Andrew Barron (Yale University, USA), Mirta Benšić (Department of Mathematics, University of Osijek, Croatia), Dragan Jukić (Department of Mathematics, University of Osijek, Croatia), Karlo Emmanuel Nyarko (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, University of Osijek, Croatia), Safet Hamedović (Faculty of Metallurgy and Materials, University of Zenica, BiH), Kristian Sabo (Department of Mathematics, University of Osijek, Croatia), Petar Taler (Department of Mathematics, University of Osijek, Croatia)

      Project duration: 1 March 2017 - 28 February 2021

    • "Mathematics for industry network (MI-NET) (TD COST Action TD1409 ), (Department of Mathematics, J. J. Strossmayer University of Osijek - COST - European Cooperation in Science and Technology) - Project coordinator: Kristian Sabo

      Summary: Mathematics underpins all of modern science and technology but advances in mathematical research are not always applied to maximum advantage in industry. The objective of this Action is to create a Europe-wide partnership to promote collaboration in, and the benefits of, industrial mathematics. The Actiom will run industry workshops, trainings weeks, and short-term scientific missions to both academic and industrial hosts, with the general aim of increasing the interaction between industry and academia. Exploiting the mathematical knowledge and methodologies af academics will provide European industry with a competitive advantage. Universities will benefit, as mathematicians are able to focus on practically relevant and cutting edge research problems. The training of Early-Career Investigators in particular will lead to a new generation with problem solving and communication skills and collaborative links that will be essential to maintain the goals of this Action in the future long after this funding has finished.

      Programme: TD COST Action TD1409

      Project partners:

      Country

      MC Member

      Austria

      Dr Andreas BINDER

      Austria

      Prof Ronny RAMLAU

      Belgium

      Dr Patricia TOSSINGS

      Bosnia and Herzegovina

      Dr Haris GAVRANOVIC

      Bosnia and Herzegovina

      Dr Harun ŠILJAK

      Bulgaria

      Mr Tihomir IVANOV

      Bulgaria

      Prof Petar POPOV

      Croatia

      Prof Anet REZEK JAMBRAK

      Croatia

      Prof Kristian SABO

      Cyprus

      Dr Katerina KAOURI

      Cyprus

      Dr Margarita ZACHARIOU

      Denmark

      Dr Poul HJORTH

      Denmark

      Prof Maria Dolores ROMERO MORALES

      Estonia

      Prof Peep MIIDLA

      Estonia

      Mr Jens HAUG

      Finland

      Dr Simo ALI-LÖYTTY

      Finland

      Dr Matylda JABLONSKA-SABUKA

      France

      Dr Joost ROMMES

      France

      Ms Edwige GODLEWSKI

      fYR Macedonia

      Dr Tatjana ATANASOVA-PACHEMSKA

      fYR Macedonia

      Dr Biljana JOLEVSKA-TUNESKA

      Germany

      Prof Dietmar HOEMBERG

      Germany

      Prof Rene PINNAU

      Greece

      Prof Vasileios KOSTOGLOU

      Greece

      Dr Nikolaus PLOSKAS

      Hungary

      Dr András BÁTKAI

      Hungary

      Prof Istvan FARAGO

      Ireland

      Dr Miguel BUSTAMANTE

      Ireland

      Dr William LEE

      Israel

      Dr Yirmeyahu KAMINSKI

      Israel

      Dr Aviv GIBALI

      Italy

      Prof Alessandra MICHELETTI

      Italy

      Dr Rada NOVAKOVIC

      Lithuania

      Prof Raimondas CIEGIS

      Netherlands

      Dr Vivi ROTTSCHAFER

      Netherlands

      Prof Wilhelmus SCHILDERS

      Norway

      Prof Elena CELLEDONI

      Norway

      Dr Svenn Anton HALVORSEN

      Poland

      Prof Wojciech OKRASINSKI

      Poland

      Dr Agnieszka WYLOMANSKA

      Portugal

      Prof Adérito ARAÚJO

      Portugal

      Ms Margarida PINA

      Romania

      Prof Costica MOROSANU

      Romania

      Dr Ionut PORUMBEL

      Serbia

      Prof Natasa KREJIC

      Serbia

      Prof Ivan OBRADOVIC

      Slovakia

      Dr Peter FROLKOVIC

      Slovakia

      Prof Karol MIKULA

      Slovenia

      Prof Janez POVH

      Spain

      Prof Tim MYERS

      Spain

      Prof Peregrina QUINTELA ESTÉVEZ

      Sweden

      Dr Hanifeh KHAYYERI

      Sweden

      Prof Johan HOFFMAN

      Switzerland

      Dr Joerg OSTERRIEDER

      Switzerland

      Prof Wolfgang BREYMANN

      Turkey

      Prof Enis KAYIS

      United Kingdom

      Dr Robert LEESE

      United Kingdom

      Dr Hilary OCKENDON

      Team members (UNIOS): Kristian Sabo, Krešimir Burazin

      Project duration: 5 May 2015 – 4 May 2019

    • "Optimization of parameter dependent mechanical systems", (Department of Mathematics, J. J. Strossmayer University of Osijek - Croatian Science Foundation) - Project coordinator: Ninoslav Truhar

      Summary: This project is devoted to second order mechanical systems which are described by a system of differential equations: M x''(t) + D x'(t)+ K x(t) =B f(t)+E w(t), x0=x(0), v0=x'(0), where M, D, K are semidefinite Hermitian large – scale matrices, dependent on one or more real parameters, while B and E are full rank matrices with p and q columns, respectively, much smaller than n. Although the above systems have been widely investigated, there are still many interest open problems from theoretical point of view, but also from the applications itself. One of such problems is optimization of a small rank damping of different kind (passive, viscose, semi-active) from which follow open problems as positioning of dampers, optimal number of dampers, optimal dampers characteristics, etc. The majority of the research within this project will therefore be focused to: optimization of active and passive damping and optimal control of parameter dependent mechanical systems with and without external force; describing the properties of eigenvalues and eigenvectors of the corresponding parameter-dependent quadratic eigenvalue problem as well as corresponding parameter-dependent nonlinear eigenvalue problems.
      Within the problem of active and passive damping optimization and optimal control of parameter dependent mechanical systems with and without external force, we will develop a general theoretical framework which describe many important system properties, and we will construct the corresponding numerical algorithms for the calculation of desired quantites. These theoretical considerations will be related to the optimization of various damping parameters with respect to several different optimization criteria as e.g.: spectral abscissa optimization, optimization of total average energy of the system, optimization of average amplitude of displacement, optimization of average amplitude of energy and impulse response energy. Furthermore, within the stated objectives we will solve many numerical demanding problems, for example: mixed-integer nonlinear optimization problem, efficiently solving of large matrix equations (Lyapunov, Sylvester, Riccati), improving the optimization algorithms by dimension reduction. We will also consider theoretical and numerical aspects of optimization of semi-active damping problem and optimal control based on various criteria (minimization of H_2, H_infinity norms, etc.).
      Within the problem of describing the behaviour of eigenvalues and eigenvectors of the parameter-dependent quadratic eigenvalue problems, we will develop perturbation theory for the corresponding quadratic problem where we will separately consider cases when M, D, K are semidefinite Hermitian matrices, and corresponding linearized pair is diagonalizable (this means that eigenvalues of quadratic eigenvalue problem can be complex) and so called overdamped case, i.e. the case when the corresponding linearized pair is definite. Further, we plan to generalize the obtained results on the parameter dependent nonlinear eigenvalue problem. For all cases we will develop perturbation theory which will contain perturbation bounds of absolute and relative type for the eigenvalues and associated eigenvectors i.e. subspaces.
      Since the stated problems are closely related, insight into the behaviour of eigenvalues and corresponding eigenvectors will allow better understanding of the damping, or other parts of the mechanical systems, while the better understanding of optimal damping or parameters in mechanical system will clarify some important properties of mechanical systems, such as overdampness, stability etc.

      Programme: Croatian Science Foundation

      Project partners: Prof. dr. sc. Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
      Prof. dr. sc. Ivan Slapničar, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split
      Dr. sc. Nevena Jakovčević Stor, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split
      Jonas Denißen, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany

      Project members: prof. dr. sc. Ninoslav Truhar, doc. dr. sc. Zoran Tomljanović, dr. sc. Ivana Kuzmanović, dr. sc. Suzana Miodragović

      Project duration: 1. 7. 2015. – 30. 6. 2019.