Why would anyone need «neural physics»?
Well, at least one problem it solves is the sim-to-real gap, and that’s what this paper is about
NeRD is a neural simulation module that can predict future states of articulated rigid bodies, completely replacing time integration and solvers
Initially, it’s pre-trained on classic simulation data, which is cool, but doesn’t add much value by itself
The important part: it can be fine-tuned on real data to handle wear and tear, contact reality, and environmental changes, significantly improving dynamics accuracy compared to the analytical simulator
The architecture is surprisingly simple, it’s basically a GPT-2 with a tiny history window and a robot-centric tokenization
That design decision makes it fast too, faster than the analytical solution
arxiv.org/pdf/2508.15755