<p>This fake writeup pretends the goal is simple: make a neural surrogate that behaves enough like a CFD solver to be useful, but not so much like a CFD solver that it takes all afternoon to answer.</p>
<p>The model gets geometry, flow conditions, and a stern lecture from validation metrics. It returns fields, uncertainty hints, and the occasional reminder that conservation laws are not optional.</p>
<p>The interesting part is the boundary between learned approximation and solver-grounded truth: where the network is fast, where it is wrong, and how to know before a wing falls off in a slide deck.</p>
</article>
<navclass="back-link"><ahref="/projects">back to projects</a></nav>