Physics simulators just became the new GPUs
Neural networks are eating physics simulation from the inside out, and traditional HPC is about to get binned.
We’re watching the physics simulation world get completely restructured. Neural networks aren’t just accelerating traditional solvers anymore. They’re replacing the entire computational stack, from PDEs to visualisation, with learned representations that run orders of magnitude faster.
The infrastructure shift is already happening
Traditional physics simulation relies on massive compute clusters grinding through finite element methods for days. Neural approaches like PhysicsNeMo are collapsing that entire pipeline into inference calls that complete in seconds. The hardware implications are enormous. Instead of needing supercomputers for fluid dynamics, you need inference-optimised silicon.
Academic physics is getting steamrolled
Physics departments are still teaching students to derive Navier-Stokes equations by hand whilst AI labs are training models that solve fluid dynamics problems without ever seeing the underlying maths. The models don’t care about your elegant theoretical framework. They just work, and they work faster than your carefully optimised FORTRAN code ever will.
But someone still needs to validate the outputs
The dirty secret everyone’s ignoring is that neural physics models are brilliant at interpolation but terrible at telling you when they’re wrong. Traditional simulators fail gracefully with error bounds. Neural ones hallucinate plausible-looking results that could be completely wrong. We’re trading mathematical rigour for speed, and most people don’t realise what they’re giving up.
The future belongs to hybrid approaches that use neural models for speed and traditional methods for verification. Everything else is just wishful thinking.