Nicholas J Lucenti

RI-SINDy removes known Hill-type regulatory terms from the sparse-regression library and solves each coefficient separately from a drift-balance condition, the near-zero time average of the derivative along a bounded trajectory, avoiding the ill-conditioning that arises when regulatory and polynomial terms are nearly collinear over an oscillation. On Hes1 experimental data it gives the lowest trajectory error among Traditional SINDy, SR3 and Nullcline Reconstruction; on synthetic Goodwin and NF-κB models it recovers the correct sparse structure, and in a Goodwin noise sweep it still finds all 6 active terms at 15% noise.

RI-SINDy removes known Hill-type regulatory terms from the sparse-regression library and solves each coefficient separately from a drift-balance condition, the near-zero time average of the derivative along a bounded trajectory, avoiding the ill-conditioning that arises when regulatory and polynomial terms are nearly collinear over an oscillation. On Hes1 experimental data it gives the lowest trajectory error among Traditional SINDy, SR3 and Nullcline Reconstruction; on synthetic Goodwin and NF-κB models it recovers the correct sparse structure, and in a Goodwin noise sweep it still finds all 6 active terms at 15% noise.