果冻传媒

Within CASA鈥檚 SC group, we propose, analyze, develop and implement new numerical mathematics methods, particularly structure-preserving discretization methods for partial differential equations and numerical linear algebra methods for linear systems of equations. We carry over these methods to Computational Science and Engineering (CSE), for application to particularly fluid-structure-interaction and energy-conversion problems.

Traditionally, CSE is model-driven; based on mathematical models of first principles (physical laws for instance). Because of the immense growth in the availability of data, CSE is becoming data-driven as well, with an important role in this for neural networks. Within CASA鈥檚 SC group, neural-network technology in CSE is also studied. One of our research goals is to further develop neural networks in the context of CSE, combining data- and model-driven approaches, hand-in-hand with the development of more theory for a rational and trustworthy use of data and neural networks within CSE.