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 →
Single target with VCO cubic nonlinearity and temperature drift. States: [I_beat, Q_beat, phi_vco, phi_error, agc, T_drift]. VCO: dphi/dt = 2pi(f0 + k1*V + k3*V^3). Harmonics at 3f, 5f create spectral leakage. Thermal drift adds slow mode. 10 chirp periods.
Stove (1992) §4 (VCO linearity); Jankiraman (2018) Ch. 3
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):
I_b, Q_b, phi_err, fe_sq_acc, agc, T_drift = y
d = np.empty(6)
V_tune = V_tune_amp * (t / T_chirp)
vco_freq = f0 + k1 * V_tune + k3 * V_tune ** 3
f_ideal = f0 + chirp_rate * t
freq_error = vco_freq - f_ideal
temp_detune = 1e3 * (T_drift - 25.0)
effective_rate = omega_b + omega_d + _TWO_PI * (freq_error + temp_detune)
d[0] = -effective_rate * Q_b
d[1] = effective_rate * I_b
d[2] = _TWO_PI * freq_error
d[3] = freq_error * freq_error
power = I_b * I_b + Q_b * Q_b
d[4] = (1.0 - agc * power) * inv_agc_tau
d[5] = -T_drift * inv_tau_th + P_over_C
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: low
Default noise: low
Recommendation snapshot
Clean best: SciPy BDF
Noisy best: SciPy RK45
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: FMCW VCO Nonlinearity + Thermal Drift (fmcw-vco-nonlinearity-thermal-drift)
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 BDFSciPy | 100% | — | 429,214 | 14.59 s | 0.809 |
| 2 | SciPy RK45SciPy | 100% | — | 421,718 | 3.46 s | 0.809 |
| 3 | SciPy LSODASciPy | 100% | — | 119,111 | 586 ms | 0.809 |
| 4 | SciPy DOP853SciPy | 100% | — | 150,530 | 1.13 s | 0.809 |
| 5 | CVODE BDFexternal | 100% | — | 127,897 | 1.12 s | 0.809 |
| 6 | CVODE Adamsexternal | 100% | — | 102,481 | 894 ms | 0.809 |
| 7 | Tsit5external | 100% | — | 357,576 | 27.82 s | 0.809 |
| 8 | SolvSRK | 100% | — | 589,939 | 1.79 s | 0.809 |
| — | SciPy RadauSciPy | 0% | — | — | — | — |
| — | SciPy RK23SciPy | 0% | — | — | — | — |
At Clean, best balanced arm is SciPy BDF · SolvSRK survival 100%.
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_fmcw_vco_nonlinearity_thermal_drift_2026,
title = {Resonix Evidence Portal: FMCW VCO Nonlinearity + Thermal Drift},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/fmcw-vco-nonlinearity-thermal-drift}},
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