SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
APN with ZEM/t_go^2 command — diverges as t_go->0, capped at t_go_min=0.1s. Adaptive gain N_eff adjusts to estimated maneuver.
Medians below are from a governed Deep FIELD campaign — not ad-hoc tuning runs.
Zarchan Ch. 8 (augmented PN); Yanushevsky Ch. 4 (optimal guidance)
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
def _clamp(x: float, lo: float, hi: float) -> float:
return max(lo, min(hi, x))
def rhs(t, y):
rx, ry, rz = y[0], y[1], y[2]
vx_i, vy_i, vz_i = y[3], y[4], y[5]
t_go = y[6]
N_eff = y[7]
d = np.empty(8)
R = np.sqrt(rx * rx + ry * ry + rz * rz)
if R < _RANGE_GUARD:
d[:] = 0.0
return d
vx_rel = vx_i - vtx
vy_rel = vy_i - vty
vz_rel = vz_i - vtz
R_dot = (rx * vx_rel + ry * vy_rel + rz * vz_rel) / R
V_c = -R_dot
t_go_safe = max(t_go, t_go_min)
t_go_sq = t_go_safe * t_go_safe
zem_x = rx + vx_rel * t_go_safe
zem_y = ry + vy_rel * t_go_safe
zem_z = rz + vz_rel * t_go_safe
a_x = N_eff * zem_x / t_go_sq
a_y = N_eff * zem_y / t_go_sq
a_z = N_eff * zem_z / t_go_sq
a_mag = np.sqrt(a_x * a_x + a_y * a_y + a_z * a_z)
if a_mag > a_max:
scale = a_max / a_mag
a_x *= scale
a_y *= scale
a_z *= scale
wx = np.interp(t, _noise_ts, wind_noise[0])
wy = np.interp(t, _noise_ts, wind_noise[1])
wz = np.interp(t, _noise_ts, wind_noise[2])
d[0] = vx_rel
d[1] = vy_rel
d[2] = vz_rel
d[3] = a_x + wx
d[4] = a_y + wy
d[5] = a_z + wz
d[6] = -V_c / max(R, _RANGE_GUARD)
zem_mag = np.sqrt(zem_x**2 + zem_y**2 + zem_z**2)
maneuver_ratio = zem_mag / (a_tgt_est * t_go_sq + 1.0)
N_target = N_eff_0 + 2.0 * maneuver_ratio
d[7] = _clamp((N_target - N_eff) / tau_n, -10.0, 10.0)
return dCanonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: extreme
Default noise: medium
Recommendation snapshot
Clean best: SolvSRK
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Augmented PN with Time-to-Go Estimation (augmented-pn-with-time-to-go-estimation)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SolvSRK | 100% | 10.0 | 128,013 | 853 ms | 0.858 |
| 2 | CVODE Adamsexternal | 100% | 7.3 | 31,943 | 455 ms | 0.792 |
| 3 | SciPy RK23SciPy | 100% | 7.2 | 45,560 | 746 ms | 0.791 |
| 4 | SciPy BDFSciPy | 100% | 7.0 | 41,363 | 1.68 s | 0.785 |
| 5 | FBDFexternal | 100% | 6.9 | 51,864 | 6.59 s | 0.782 |
| 6 | CVODE BDFexternal | 100% | 6.7 | 34,627 | 498 ms | 0.779 |
| 7 | SciPy LSODASciPy | 100% | 6.6 | 57,654 | 634 ms | 0.777 |
| 8 | Vern9external | 100% | 6.6 | 132,050 | 7.45 s | 0.776 |
| 9 | SciPy RadauSciPy | 100% | 6.4 | 142,672 | 4.04 s | 0.771 |
| 10 | Vern7external | 100% | 6.2 | 86,692 | 6.32 s | 0.767 |
| 11 | Tsit5external | 100% | 6.2 | 38,466 | 4.82 s | 0.766 |
| 12 | SciPy DOP853SciPy | 100% | 6.1 | 97,142 | 1.31 s | 0.765 |
| 13 | SciPy RK45SciPy | 100% | 5.7 | 25,178 | 210 ms | 0.754 |
| 14 | TRBDF2external | 100% | 5.0 | 17,088 | 5.80 s | 0.739 |
At Clean, best balanced arm is SolvSRK.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
SolvScout · free
This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here - including when a SciPy arm wins.
SolvSRK · 30-day trial
SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial - same binary you'd ship after purchase.
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is - a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_augmented_pn_with_time_to_go_estimation_2026,
title = {Resonix Evidence Portal: Augmented PN with Time-to-Go Estimation},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://www.resonix.io/evidence/problems/augmented-pn-with-time-to-go-estimation}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status · Deep FIELD · Innovation velocity
Governed research, licensable IP, partner programs.
Company brief, evidence, and data room access.