The Council for Scientific and Industrial Research (CSIR) in South Africa is one of the leading scientific and technology research, development and implementation organisations in Africa. It undertakes directed research and development for socio-economic growth.

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CSIR Modelling and Digital Science

Advanced mathematical modelling and simulation

This area will encompass applications using mathematical sciences, scientific computing and information and communications technology (ICT) platforms to model phenomena ranging from a drug delivery system in the human body to atmospheric, geophysical and oceanographic phenomena aimed at helping us understand climate change. It comprises the following research groups:

  • Climate modelling – This involves predictive modelling using fluid dynamics and other mathematics tools for weather and climate change; and the use of dynamical systems modelling and other tools to study possible futures.

  • Digital patient - This involves patient-specific predictive modelling to aid medical experts in managing cardiovascular and other common ailments. In addition, it will study computational tools for the development of treatment by general practitioners and specialists

  • Financial modelling – This involves risk analysis using stochastic differential equations and other tools.

  • Water resource modelling - Predictive modelling and simulation of water budget and management.

  • Energy systems modelling - Predictive modelling and simulation of energy systems and demand.

Researcher Profile

Starting off as an assistant labourer in his fatherís bricklaying business did not deter Dr Donald Mkhonto from attaining success in a highly scientific research career.

Dr Siyakatshana is a senior researcher at CSIR Modelling and Digital Sciences' Advanced Mathematical Modelling (AMM) competency area .

About this section
Overview: CSIR Modelling and Digital Science
Contact details
Research groups
Advanced mathematical modelling and simulation
Information security
Mobile intelligent autonomous systems
Two water demand prediction models put to the test
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