Data centers don’t just need more power. They need more compute from every MW without compromising uptime. Power, cooling, controls, equipment, workloads, and maintenance are tightly coupled but managed separately. We’re building Edviro: a world model to operate them as one.

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.

if you’re a cracked high schooler listen up we’re launching the beacon fellows program, a selective fellowship for students who wanna drive sustainability and transparency for taxpayer money in their schools using energy data. details + apply here: beacon.edviroenergy.com