Can the algorithms deal with invalid data, e.g., NaN or inf?

Yes. By default, whenever a simulation returns at least one NaN or inf, it is completely excluded from the training data. In other words, the simulation is simply discarded.

In cases where a very large fraction of simulations return NaN or inf, discarding many simulations can be wasteful. There are two options to deal with this: Either you use the RestrictionEstimator to learn regions in parameter space that do not produce NaN or inf, see here. Alternatively, you can manually substitute the ‘invalid’ values with a reasonable replacement. For example, at the end of your simulation code, you search for invalid entries and replace them with a floating point number. Importantly, in order for neural network training work well, the floating point number should still be in a reasonable range, i.e., maybe a few standard deviations outside of ‘good’ values.

If you are running multi-round NPE (SNPE), however, things can go fully wrong if invalid data are encountered. In that case, you will get the following warning

When invalid simulations are excluded, multi-round SNPE-C can leak into the regions
where parameters led to invalid simulations. This can lead to poor results.

Hence, if you are running multi-round NPE and a significant fraction of simulations returns at least one invalid number, we strongly recommend manually replacing the value in your simulation code as described above (or resorting to single-round NPE, or using a different sbi method entirely).