No clear winner. The survival gap is under 10 percentage points and the balanced-score gap is under 0.05, so neither SolvSRK nor the best baseline clears the win threshold. Either works — choose on cost, licensing, or integration effort. All verdicts →
3D engagement with first-order actuator lag (tau=0.02s) and aerodynamic drag. Stiffness ratio ~50 from fast actuator vs slow kinematics.
Zarchan Ch. 11 (autopilot lag); Siouris Ch. 7 (actuator dynamics)
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 rhs(t, y):
rx, ry, rz = y[0], y[1], y[2]
vx_i, vy_i, vz_i = y[3], y[4], y[5]
a_cmd_prev = y[6]
a_actual = 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
R2 = R * R
omega_x = (ry * vz_rel - rz * vy_rel) / R2
omega_y = (rz * vx_rel - rx * vz_rel) / R2
omega_z = (rx * vy_rel - ry * vx_rel) / R2
omega_mag = np.sqrt(omega_x**2 + omega_y**2 + omega_z**2)
if omega_mag > _LOS_RATE_CLAMP:
scale = _LOS_RATE_CLAMP / omega_mag
omega_x *= scale
omega_y *= scale
omega_z *= scale
a_cmd_mag = N * V_c * np.sqrt(omega_x**2 + omega_y**2 + omega_z**2)
V_int = np.sqrt(vx_i**2 + vy_i**2 + vz_i**2 + 0.01)
F_drag = drag_coeff * V_int * V_int
omega_total = np.sqrt(omega_x**2 + omega_y**2 + omega_z**2 + 1e-12)
a_dir_x = omega_x / omega_total
a_dir_y = omega_y / omega_total
a_dir_z = omega_z / omega_total
d[0] = vx_rel
d[1] = vy_rel
d[2] = vz_rel
d[3] = a_actual * a_dir_x - F_drag * vx_i / V_int
d[4] = a_actual * a_dir_y - F_drag * vy_i / V_int
d[5] = a_actual * a_dir_z - F_drag * vz_i / V_int
d[6] = (a_cmd_mag - a_cmd_prev) * inv_tau
d[7] = (a_cmd_prev - a_actual) * inv_tau
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: high
Default noise: low
Recommendation snapshot
Clean best: SciPy DOP853
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: 3D PN with Actuator Dynamics (tau=0.02s) (3d-pn-with-actuator-dynamics-tau-0-02s)
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 | SciPy DOP853SciPy | 100% | 11.3 | 8,522 | 86 ms | 0.888 |
| 2 | SciPy RadauSciPy | 100% | 10.7 | 4,221 | 95 ms | 0.875 |
| 3 | SolvSRK | 100% | 10.3 | 9,793 | 151 ms | 0.864 |
| 4 | SciPy RK45SciPy | 100% | 10.2 | 9,776 | 105 ms | 0.862 |
| 5 | Tsit5external | 100% | 9.9 | 8,184 | 1.48 s | 0.855 |
| 6 | SciPy RK23SciPy | 100% | 8.5 | 9,605 | 120 ms | 0.822 |
| 7 | CVODE Adamsexternal | 100% | 8.0 | 862 | 15 ms | 0.810 |
| 8 | SciPy BDFSciPy | 100% | 7.4 | 1,398 | 45 ms | 0.795 |
| 9 | SciPy LSODASciPy | 100% | 7.0 | 2,227 | 18 ms | 0.786 |
| 10 | CVODE BDFexternal | 100% | 6.7 | 591 | 12 ms | 0.779 |
At Clean, best balanced arm is SciPy DOP853 · SolvSRK survival 100%, SCD 10.3.
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_3d_pn_with_actuator_dynamics_tau_0_02s_2026,
title = {Resonix Evidence Portal: 3D PN with Actuator Dynamics (tau=0.02s)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/3d-pn-with-actuator-dynamics-tau-0-02s}},
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