Dr Peter Waegli has always used mathematical modelling and simulation to solve problems. He has found that the process of developing computational (physics) models is often the key to understanding the basics governing a physics or engineering problem at hand and hence to its solution. Such model building often starts from "back-of-the envelop" calculations and ends with comprehensive analytical and/or numerical models. It is of high importance that the tool used, supports such an approach and does not impose adoption of a specific problem solving philosophy.
Over many years of successfully developing and using computational models, Dr Waegli has used programming languages like Fortran and Visual Basic and excellent programs such as Maple® and Mathematica®, but has found that often Excel supports the process from simple calculations to sophisticated models best and much simplifies the exchange of simulations with clients and fellow researchers. Many of the applications described under "Development Projects" are hence based on Excel models. Such models also play an important role in the projects described below.
In his own projects Dr Waegli is currently exploring new simulations approaches in three areas:
(I) Development of Excel based photonics models for educational purposes and application developers using (off-the-shelf) photonics technologies. The use of an affordable and ubiquitous tool will help to broaden the understanding for optical technologies and their governing relationships and will contribute to the proliferation of photonics applications using cheap and powerful components, which are becoming ubiquitous at fast pace.
(II) Simulating the tight interaction of physical and cyber functions in cyber physical systems. Interacting (small) neural network based meta-models of physical and cyber functions are used to simulate cyber physical components. Such components are then networked to study the behaviour of larger scale systems. First applications have been realized for semi-autonomous photovoltaic mini-grids (see also "Publications").
(III) Resistor network simulations to explore possibilities with new emerging components - such as memristors and to study so-called neuromorphic systems for image and signal analysis purposes.