Description: Personal website of Arno Strouwen
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Most experimental design focusses on parameter precision, where the model structure is assumed known and fixed. But arguably finding the correct model structure is the part of the modelling process that takes the most effort. In this blog we will look at automating this process using symbolic regression, and to do this with gathering too much data.
Gradients are useful for efficient parameter estimation and optimal control of dynamic systems. Calculating these gradients requires sensitivity analysis. Sensitivity analysis for dynamic systems comes in two flavors, forward mode and adjoint (reverse). For systems with a large number of parameters adjoint sensitivity analysis is often more efficient [1] . I find that the traditional way of deriving adjoints for ordinary differential equations, such as [3] , leaves me with little intuition what these equati
Optimal experimental design is an area of statistics focused on constructing informative experiments. In this tutorial we introduce the necessary tools to construct such informative experiments for dynamic systems using only 100 lines of Julia code. We will work with a well-mixed fed-batch bioreactor as an example system. We have quite a bit of domain knowledge how to model the behavior of such a reactor. The reactor has three dynamic states: the substrate concentration $C_s$, the biomass concentration $C_x