
Read what I read, code what I code. I share lots of papers/code for topics in artificial intelligence/machine learning applied to engineering (CFD, FEA, numerical methods, aerodynamics, turbulence modeling, & more). I will publish enough to keep you busy.
| Platform | Pricing | Freemium | Publishes | Twice weekly | |
|---|---|---|---|---|---|
| Issues | 95 | Founded | a year ago | Last Issue | 3 days ago |
| Active | |||||

Everybody doing more simulation wants higher fidelity - more accurate solver, finer mesh resolution to pickup on details better, etc. ML surrogates have been show to help map between the simulations they can run (coarse, moderate fidelity...
Automotive Aerodynamics Code Tutorial
Here I present a code tutorial applying the “AdaField” paradigm to the DrivAerML dataset with the Transolver++ model. This explains the approach, shares the code & dataset, and walks through this novel...
Physical AI hands-on
SUMMARY
PDFs and brief summary
Wow, only 12 new publications in the last 14 days for ML in CFD. Goodluck to us 🫠 .
Code tutorial to follow
Paper: AdaField: Generalizable Surface Pressure Modeling with Physics-Informed Pre-training and Flow-Conditioned Adaptation
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The writers behind this newsletter.
\ud83d\udc4b Hello. My name is Justin and I am head of physical ai at CoreWeave. Read what I read - code what I code. I love applying #ai and #machinelearning to #engineering and scientific simulation.
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