Science at the intersection of data, modelling, and computation

Today computational science [1] becomes a major multidisciplinary scientific direction bringing together diverse areas. Appeared at the intersection of computer science, information technologies, and mathematical modeling, it extended the range of scientific tools available for scientists in many research fields from traditional natural sciences to new applications in medicine, social sciences, and humanities. In addition, recent advances in computational science are widely applied in engineering and industry, see for instance Simulation Based Engineering Science [2].

Commonly, computational science grows as a combination of modeling and simulation with information technologies for computational-intensive solutions development (high-performance, distributed, hybrid, etc. computing). Nevertheless, currently, a multitude of studies are faced with a large amount of data available through observation, measurement or as a result of earlier modeling and simulation. Under these conditions, we can talk about a new paradigm in science, namely data-intensive scientific discovery [3]. This direction is tightly connected with the achievements of Big Data technologies in the area of model-based data analysis and joining data processing with high-performance and distributed computing [4].

On the other hand, lately, a large amount of available data forces rapid development of intelligent data-driven technologies including machine learning and data mining, which empower computational science in various ways. Beside direct usage of data-driven predictive models for assessment of various characteristics of system under investigation, intelligent technologies may be used in different scenarios for: a) management of complex models; b) substitution of computationally-intensive models; c) exploration or interpolation of model parameters and data; d) prediction of model characteristics (including performance, uncertainty, etc.). One of the powerful techniques deployed by computational science is surrogate modeling [5,6].

Giving a significant impact to the existing methods, the mentioned approaches raise many important issues related to the higher connectivity between areas within computational science, namely, data analysis, computing, and modeling. Having an excessive amount of available data, multiscale and complex models, and very diverse computational resources, a computational scientist needs a higher-level automation, adaptation, and configuration of a solution. Various intelligent technologies could be used for these purposes developing system-level management of complex solutions including data, models, and computational resources. One of the ways is exploiting evolutionary and co-evolutionary computation for complex model management and structure identification [7]. Considering the symbiosis of data, modelling, and computation as an important driver for further development of computational science, the theme for ICCS 2018 was selected, “Science at the Intersection of Data, Modelling, and Computation”.

The International Conference on Computational Science (ICCS)¹ brings together researchers and scientists working in fundamental computer science disciplines and in various application areas, who are pioneering computational methods in sciences such as physics, chemistry, life sciences, and engineering, as well as in arts and humanities. Since its inception in 2001, the ICCS forms a space where the problem domains, IT, and modeling join together to discuss the present and future research directions. ICCS is an A-rank² conference in the CORE classification.