January Literature Summary w/ PDFs
Here's what I read in January. You can grab all the PDFs here, or simply just read the summary sections. The focus is AI/ML for engineering simulation (aerodynamics, mechanical, etc.)
My PhD advisor left a lot of impressions on me, one of which being the importance of routinely reading literature on your subject matter. At the time, the portrayal was that your propensity to produce quality research/performance in the lab was mainly limited by your lack of time spent reading scientific publications. I try my best to still carry this lesson forward, despite that now I am far from working in a lab in graduate school. I feel it’s vital to stay reading; whether it’s to keep up to date with innovations in AI/ML, or whether it’s to dive deep in some fundamental concepts to ensure you have a proper understanding.
In January, you can see me straddling two horses in what I am reading:
Fundamental building blocks of AI/machine learning components. Nothing specific to aerospace or mechanical engineering applications. Clearly, I am surveying all that is out there for some pretty fundamental modeling choices to make every time we craft a machine learning model/project— and really dig into it!
AI/ML innovations specifically in engineering simulation (like computational fluid dynamics).
Enjoy this repo of 80+ PDFs with summaries.
FWIW, as 2024 came to a close I analyzed how much I was reading and was creative to come up with a way to try to read more (without taking away time from my daughter, hobbies, or loved ones). For me, an e-reader made a huge difference to min-max my time. I load my papers on it at the end of each work day, and carry it around habitually. It really helped me reduce my phone screentime (truly wasted time) by swapping with random e-reader time. Here is the e-reader I use. I am very happy with it: https://amzn.to/4ar7gxM (pro tip: get a cover so you keep it fresh).