SymbolicRegression.jl: Distributed High-Performance Symbolic Regression in Julia

After UDE training, riva2026random suggests Cranmer's SymbolicRegression.jl to extract closed-form expressions N(P)X from sampled state-velocity pairs, while warning that gauge dependence of the latent coordinates can defeat sparsity unless one regresses on gauge-invariant features.