Numerical Analysis & Computational Science
IACM has a long tradition in Numerical Analysis and Scientific Computing research and focuses on both fundamental research as well as on applied, industrial applications. It includes experts in various computational methods for differential equations, stochastic and multiscale models. Research directions include: space-time adaptive algorithms for the numerical approximation of solutions of nonlinear PDEs exhibiting singular behavior, the development and study of novel, rigorous, structure-preserving computational methods for various applications, the development of novel multiscale numerical methods and of models across scales, methods for dispersive wave propagation, advanced time stepping techniques and uncertainty quantification.
The biomechanics team research is related to cellular and tissue modelling and dynamics and is focused on developing models of the cardiovascular system that include non-Newtonian blood rheology and non-linear arterial wall elasticity to assess the impact of implantation of devices for endovascular repair on cardiac workload and in cell mechanotransduction to assess flow induced stress effects on cell proliferation and migration, gene expression and cell growth direction to support the design of nanoscaffolds for spatially directed tissue growth.
The Data Science Group focuses on a) methodological research questions related to data science and algorithmic artificial intelligence, b) foundational questions regarding the best achievable performance limits in various tasks of data modeling, analysis and inference, and c) interdisciplinary research questions from a wide variety of research domains, including Social Sciences, Geo-Sciences, Bioinformatics and Biomedical Engineering, Environmental and Transportation Engineering, among others. Current activity areas include frequentist and Bayesian spatio-temporal modelling, high-dimensional and functional data analysis, uncertainty quantification for various models, time-series forecasting and online predictive, data assisted computational modelling and anomaly detection algorithms. Researchers from the Data Science Group participate in the Statistical Learning Lab which fosters multidisciplinary collaborations, develops software products, research publications and patents and contributes to the education and training of students and young researchers.
The division has a long track record in joint collaboration with high-value industries. To maintain and further expand a strong industry-academia collaboration ecosystem, the group welcomes discussion with interested industrial partners in Greece and abroad. The Numerical Analysis & Computational Science Division consists of three groups and one laboratory: