I have a faster solution for N-body gravity with orbital resonance that predicts the perihelion of Mercury than gravity from QED scattering, with Gr_C(2,4) in Lean 4 and Rust/WASM that I haven't yet published.

I noticed "Kepler" in the title and cool story about Kepler so now I'm mentioning it;

"Grassmannian Gravity from QED & Gauge Scattering Amplitudes: Complex Gr_C(2,4) Orbital Precession, Mercury's Perihelion, N-Body Dynamics, and Dilatant Dark Fluid (DDF) Unification in Lean 4" (2026) westurner/sustainablefactory/docs/grassmannian-gravity-from-qed-scattering-paper.md at main · westurner/sustainablefactory https://github.com/westurner/sustainablefactory/blob/main/do... :

> An arXiv-ready formal verification paper deriving planetary orbits, Mercury's 42.98 arcsec/century perihelion precession, and N-body gravitational dynamics from flat-space QED/gauge scattering amplitudes, BCJ double copy on Gr_C(2,4), and Dilatant Dark Fluid (DDF) hydrodynamics in Lean

There's already gravity that fits the Perihelion of Mercury from QED scattering (Partanen, M., and Tulkki, J. (2025). "Gravity Generated by Four One-Dimensional Unitary Gauge Symmetries and the Standard Model" / "Perihelion Precession of Planetary Orbits Solved from Quantum Field Theory"). FWIU, I've here contributed the use of Grassmanian Plückers and a Complex Grassmannian - GrC(2,4) both with and without connections to DDF Dilatant Dark Fluid (Fedi).

I had created a simple particle wave visualization tool with 3js and then on a whim AI-added a double slit and a detector screen, and I asked for Huygens-Steiner Parallel-Axis Intensity as the model to simulate superposition because of [Qian and Izadi] and then I had to keep working on that instead. First the model differencing in the simulator in JS to compare the difference in model predictions in the double-slit experiment against Pilot Wave Theory and Wigner function and then with Fraunhofer and Lean 4 and Rust and a crisp symbolic error term for each model of photonic and electronic superposition. It looks like GrC(2,4) is the most efficient model for photonic and electronic superposition, too.

"Huygens–Steiner Barycentric Coherence, Complex Gr_C(2,4) Plücker Channels, and Multi-Model Double-Slit & OAM Relative Predictive Error in Lean 4" (2026)

sustainablefactory/docs/grassmannian-huygens-double-slit-oam-paper.md at main · westurner/sustainablefactory https://github.com/westurner/sustainablefactory/blob/main/do... :

> An arXiv-ready formal verification paper uniting Qian–Izadi Huygens–Steiner parallel-axis coherence, complex Gr_C(2,4) Plücker double-slit interference, de Broglie–Bohm pilot-wave hydrodynamics, Wigner phase-space marginals, and Orbital Angular Momentum (OAM) relative predictive error in Lean

Useful to create a devcontainer with lean4 and rust. Still working through verifying the URLs of each citation to publish some of this paper series per the latest terms for AI articles.

Keplearn: Extract Equations from Data in Seconds