
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 | 88 | Founded | a year ago | Last Issue | 6 days ago |
| Active | |||||

New aerodynamics dataset released!
HiLiftAeroML is a brand new dataset made public only yesterday. We’re going to train some models to it and share the code below. When I gained access to it I knew I wanted to speedrun a tutorial and post...
ML Surrogate code/dataset to learn structural behavior
This tutorial explores how to train NVIDIA PhysicsNeMo’s DoMINO model on SimJEB, a dataset of simulated jet engine bracket designs. At a high level, the goal is to build a neural surro...
Topic == SciML Surrogates in CFD/FEA
The most interesting thread this period is that “geometry” is becoming less a nuisance variable and more the central design object. AeroJEPA is the cleanest example: instead of mapping geometry directly...
5 models following-up from literature review
In my last post, “Literature Review: The Best ML Models for Learning Geometry”, I covered a number of publications for various models that can learn and reconstruct geometries. As follow-up, I w...
PDFs, paper summaries, and select figures
Applying ML surrogates to problems in industry implicitly requires models that can learn complex, large, irregular geometries. Think complex contoured car bodies, multi-stage gas turbine blades, or...
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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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