Science methods

SARA Systems currently builds up expertise in all scientific areas. However, there is a focus on computational methods:

Mathematical Modelling: Mathematical Modelling is needed in most parts of science, and in almost any industrial applications. Mathematical Modelling is important for general management of systems, like traffic applications, city management, distribution networks etc. Many traditional production processes can be optimized (Industry 4.0) with the help of mathematical models, or complex processes, which have not yet projected to mathematical models, can be investigated by computational tools once a successful mathematical model can be established.

Scientific Computing: In most cases, in addition to mathematical modelling, computational methods are needed in scientific projects and industrial applications. Most mathematical models have a complexity far beyond the understanding of the human brain, in these cases computational methods are needed to understand the implications of the model. There is an intimate connection to data science, as data are needed to parametrize the mathematical model, and this is in itself a part of scientific computing.

Data Science: Data Science is at the core of the empirical scientific method. It has also increased in relative importance as the amount of data in science and industry is rapidly growing, and generally more easily available in electronic form. The foundation of data science is still statistics, but at present many other methods exist on top, linked to different mathematical structures, such as topological data analysis, or large-scale computational methods related to Big Data approaches.

Machine Learning and Artificial Intelligence: In cases where mathematical models are not suitable, because the underlying problem situation is too complex, or the basic problem setting is varying too much in time, or has too many parameters to adjust, the methods of choice are generally machine learning techniques. For questions involving decision making and adaptation, the rapidly evolving area of artificial intelligence is the best choice. All of these methods can also be combined with mathematical modelling, scientific computing and data science technology.

Expert Systems: This is the high-end software development at SARA Systems. It combines aspects of data bases, mathematical modelling, scientific computing, data science, machine learning, and artificial intelligence. With modern computing technology and advances in scientific research, expert systems can be established in nearly any area of expertise, like micro- and macro-economic planning, financial markets, traffic control, or health care. Due to their nature, expert systems usually require more development time, and require constant maintenance.