Math in Industry
Mathematics is used in industry, business, NGOs and governmental organizations in a multitude of ways. But it can be difficult to know exactly what different types of mathematical analysis can do for an organization. To assist companies and their representatives in considering potential areas of interaction, we present a short description of some of those areas of expertise of the academics and researchers associated with CIM.
The study of the collection, analysis, interpretation, presentation, and organization of data. Statistics have become increasingly important, due to the ease of collecting large amounts of data. Statistics are crucial part of industrial process and are used to control and analyze the performance of products and process.
In scientific computing, computational methods are developed, analyzed, and implemented for simulation and data analysis in physics, chemistry, biology, geophysics, finance, and engineering. The mathematical models are deterministic or stochastic, often written as partial differential equations or as optimization problems. The algorithms are usually deterministic but for some hard problems, stochastic Monte Carlo methods are the only alternative. Characteristics of a successful algorithm are efficiency, accuracy, and robustness. The methods are implemented in software suitable for high performance computing and cloud computing.
Technology systems can refer to material objects of use to humanity, such as machines, hardware or utensils, but can also encompass broader themes, including systems, methods of organization, and techniques. (From Wikipedia – Technology Systems)
Automation and Control
Automatic control is a an engineering field that has become important during past decades due to its application in many industrial fields. Today, automation and control is an interdisciplinary and broad active research subject, combining theoretical aspects in mathematics, optimization, signal processing and system identification to deals with industrial challenges and new applications.
The extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. (From Wikipedia)
Machine Learning and Data Analysis
Machine learning applies advanced statistical techniques to large data sets. It is used for predictive analytics, system modeling and control, and exploratory data analysis. Given our our data-saturated environment, there are vast numbers of potential applications, and concrete examples include industrial diagnostics (root cause analysis of defects, quality control, maintenance optimization), categorization of stakeholders (customers, end-users, employees, etc), for individualized treatment, and strategic analysis.
The selection of a best element (with regard to some criteria) from some set of available alternatives. (From Wikipedia – Optimization)
Simulation and Modeling
Simulation modeling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. Simulation modeling is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand. (From Wikipedia – Simulation Modeling)
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. (From SAS: Data Visualization: What it is and why matters)