Modern applications in computational science are governed by multi-scale and partially unknown physically processes that are too complex or poorly understood to be explicitly represented by the governing equations or numerical methods. The plummeting cost of sensors, computational power, and data storage in the last decade offers new opportunities for data-driven modelling of such physical systems. However, while both physical modelling and purely data-driven methods are active independent research areas, little attention has been paid to the intersection of the two. In order to enable a shift towards simulation models that are either parametrised or controlled by data-driven algorithms, there is a pressing need for new mathematical tools, new numerical abstractions and new algorithms. The ambition of DataSim is therefore to design efficient algorithms to enable data-driven simulation described by partial differential equations. Specifically, we will propose simulation models that consist of partial differential equations coupled to machine learning models build using artificial neural networks. We will then design new algorithms for model identification, and adaptive control methods for partially unknown and dynamically changing physical systems. Based on these algorithms, we will develop a general software framework for specifying, evaluating and training such models. The capabilities of our approach will be demonstrated by developing a weather precipitation model from crowd-sourced weather station data.
Project leader: Simon Funke
Category: Øvrige forskningsinstitutter
Institution: SIMULA RESEARCH LABORATORY AS