Next to constraints resulting from how the experimental equipment operates, these are also important for simulations . Consider the case when optimizing a reactor in silico with CFD. When defining the reactor geometry for CFD it is not trivial that every type of geometry is feasible to simulate nor that it can be properly meshed or that the results are mesh independent. When these constraints are non-trivial, a separate machine learning model can be trained to learn the constraints and enforce the viability of the simulations .