Models are everywhere, and especially when it comes to the energy and climate transitions. They provide a rationale for decision making, and constitute the standard way to test and improve our understanding. Yet, what a “model” is can be very different from one actor to another. Furthermore, models should be used with methodological care: any model is developed to address specific questions, in a specific validity range, with specific assumptions. However, while the output and conclusions of the models are often readily used and debated, the methodology and the validity of hypothesis and results are not often enough closely scrutinized.
As scientists involved in the energy and climate transitions, you will have to deal with many different models – whether you developed them yourselves or simply use their results. The aim of this course is to provide you with a critical methodology based on a physicist’s toolbox to help you use these models as wisely as possible.
As a note of caution : this is not a course in applied mathematics : efficient programming is important, as is a careful choice of algorithms, but this comes after the general framework is chosen and the direction set. Nor is it a course on big data analysis. An efficient algorithm can produce irrelevant or wrong data, and using up to date data analysis tool will not help to produce sensible conclusions.
The first lectures will introduce basic concepts of modelling (modeling vs simulation, prediction vs prospection…) as well as a set of physics methods relevant to the field (perturbative approach, scaling laws...). Following lectures will be presented by experts in the transition sector who will share their own experience of modelling. Students will select a case study they will investigate throughout the course, building their own model to compare and test against the existing literature.
- Teaching coordinator: Guillemoles Jean-François
- Teaching coordinator: Suchet Daniel