An interesting application of perturbation theory which I used to work with was a software application called 3DVH, which was used in radiation treatments (for cancer for example). Its inputs were the planned geometrical distribution of the radiation absorbed in the patient, and an array of measured dose points around a simulated patient, done before treatment. The output was a fairly accurate estimate of the actual dose distribution in the patient, taking into account the differences between the planned and measured doses. The reason you might want this is because if the difference between planned dose and reality was cutting it close to say, an organ or a major nerve, you'd really like to know which side of that margin your treatment was going to be on.<p>As long as the difference between the planned and measured doses was small (like, under 3%) then perturbation theory was valid for this use case, but if it got bigger, then the estimate was increasingly inaccurate. During development, software verification and validation took quite a while because of the complexities of the data sets combined with establishing the limits where perturbation theory was no longer valid. There was a lot of debate on where to set warning messages versus plain old disabling the output as invalid.<p>Training new users always seemed to bring up perturbation theory, because of this limit. Like, the first thing almost every physicist did was to create a huge dose difference to see how it would handle it. The software would pop up a message saying the difference was too big for perturbation theory to apply and so the output was disabled (this was a choice made for patient safety; better to display no data than bad data). Then the new user would ask why it wasn't working. And I would have to remind them about how perturbation theory worked...<p>Here's a few papers about it in case anyone is interested:<p><a href="https://pubmed.ncbi.nlm.nih.gov/22225277/" rel="nofollow">https://pubmed.ncbi.nlm.nih.gov/22225277/</a><p><a href="https://pubmed.ncbi.nlm.nih.gov/22830756/" rel="nofollow">https://pubmed.ncbi.nlm.nih.gov/22830756/</a>