This page is a series of tutorials showing how to solve various types of inverse problems, or model calibration problems, in Python. The physical application mostly revolves around heat transfer in building physics, hence the repo’s name.
It is not a book on the theoretical fundations of inverse methods, but a set of cases aimed at giving clear and documented examples on how to implement some of them in Python. Each tutorial is an IPython notebook, and all the code is available in the page’s GitHub repository.
Here is the list of the courses available so far (or that will soon be)
- 01_HeatConductivity: using Bayesian Inference and the pymc package to estimate the thermal conductivity of a wall
- 02_HeatFlow: linear and transient inverse heat conduction problem; calculating a heat flow from temperature measurements
- 03_MoistureTransfer: using scipy.optimize for the characterisation of heat and moisture transfer properties of a material
- 04_RC_Box: calibration of a stochastic and a deterministic RC model
You can check out my other ongoing projects on my Wordpress page
- Rouchier S., Woloszyn M., Kedowide Y., Bejat T. (2015) Identification of the hygrothermal properties of a building envelope material by the Covariance Matrix Adaptation evolution strategy, Journal of Building Performance Simulation, DOI: 10.1080/19401493.2014.996608
- Rouchier S, Busser T, Pailha M, Piot A, Woloszyn M (2017) Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm, Building and Environment, vol. 114, p. 129-139
- Rouchier S, Rabouille M, Oberlé P (2018) Calibration of simplified building energy models for paramater estimation and forecasting: stochastic versus deterministic modelling, Building and Environment, vol. 134, p.181-190
- Rouchier S (2018) Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data, Energy and Buildings, vol. 166, p. 178-195