ucsb.network

[Hursh Shah @hursheybar2]

We just hit SOTA on the standard physics-ML benchmark (Navier-Stokes 2D turbulence):

Edviro research is building world models that understand and operate energy infrastructure. Buildings are our first deployment environment.

We started with the NS2D benchmark because fluid dynamics underpins many energy systems, including airflow and industrial processes.

The goal was to rethink fundamental architectural choices. Instead of asking a neural network to learn the entire PDE update, we analytically solve the linear component and train the network only on the nonlinear increment.

The results: we hit 0.0305 (± 0.000079) across three independent seeds. Applying symmetry-averaged inference further reduces error to 0.02969 at 2x inference costs. Against code released claims, we beat them by 36.9%.

If you’re working on operator learning, physics ML, controls, or operate large-scale energy systems, we’d love to talk.

[ucsb.network @ucsbaccelerator]

sign up at https://t.co/VGiwnyqPji!

@mindfort

[Leon Khoshnevis @leonkhosh]

Amazing group office hours with @neversupervised
——
@ucsbaccelerator
@liveplayahouse

[Leon Khoshnevis @leonkhosh]

UCSB Accelerator, Day 1

Supplies provided by @cursor_ai

@ucsbaccelerator @liveplayahouse