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 →
HEL Thermal Kill (30kW.PL.300) benchmark in the defense-autonomy domain.
Canonical benchmark implementation
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):
T_tgt = max(y[0], T_ambient)
T_bat = max(y[1], T_ambient)
T_pc = max(y[2], T_ambient)
sig_d = y[3]
R = max(y[5], 1.0)
eps = max(abs(y[7]), 1e-10)
d = np.empty(8)
# Atmospheric transmission (Beer-Lambert)
tau_atm = np.exp(-beta_ext * R / 1000.0)
# Tracking efficiency (Gaussian beam, fraction on target)
spot_var = 2.0 * (eps * R) ** 2
eta_track = 1.0 - np.exp(-A_target / max(spot_var, 1e-20))
P_beam = P_laser * tau_atm * eta_track
# Target surface heating
Q_laser = alpha_abs * P_beam
Q_conv = h_conv * A_t * (T_tgt - T_ambient)
Q_rad = epsilon_t * _SIGMA_SB * A_t * (T_tgt**4 - T_ambient**4)
d[0] = (Q_laser - Q_conv - Q_rad - k_cond_bt * (T_tgt - T_bat) - k_cond_pt * (T_tgt - T_pc)) / C_t
# Battery heating (conduction from target + self-heating)
d[1] = (k_cond_bt * (T_tgt - T_bat) + I_batt**2 * R_internal - h_b * (T_bat - T_ambient)) / C_b
# PCB heating
d[2] = (k_cond_pt * (T_tgt - T_pc) + P_electronics - h_pcb * (T_pc - T_ambient)) / C_pcb
# Arrhenius damage accumulation
d[3] = A_arrhenius * np.exp(-E_activation / (_K_B * max(T_tgt, 200.0)))
# Beam power state (for diagnostics)
d[4] = (P_beam - y[4]) / 0.01
# Range decreasing
d[5] = -V_close
# Track angle rate (simplified LOS)
d[6] = V_close * 0.01 / max(R, 1.0)
# Tracking error settling
d[7] = -(y[7] - sigma_jitter * 0.5) / tau_track
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 Radau
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: HEL Thermal Kill (30kW.PL.300) (hel-thermal-kill-30kw-pl-300)
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 RadauSciPy | 100% | 14.4 | 2,426 | 65 ms | 0.962 |
| 2 | SolvSRK | 100% | 11.8 | 1,487 | 26 ms | 0.900 |
| 3 | CVODE BDFexternal | 100% | 9.5 | 393 | 22 ms | 0.846 |
| 4 | SciPy LSODASciPy | 100% | 9.5 | 608 | 5 ms | 0.845 |
| 5 | SciPy DOP853SciPy | 100% | 9.3 | 3,146 | 34 ms | 0.840 |
| 6 | SciPy RK23SciPy | 100% | 9.2 | 3,836 | 52 ms | 0.837 |
| 7 | CVODE Adamsexternal | 100% | 9.1 | 669 | 22 ms | 0.837 |
| 8 | SciPy BDFSciPy | 100% | 9.1 | 756 | 31 ms | 0.836 |
| 9 | Tsit5external | 100% | 8.4 | 2,412 | 1.08 s | 0.819 |
| 10 | SciPy RK45SciPy | 100% | 8.3 | 2,180 | 25 ms | 0.818 |
At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 11.8.
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_hel_thermal_kill_30kw_pl_300_2026,
title = {Resonix Evidence Portal: HEL Thermal Kill (30kW.PL.300)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/hel-thermal-kill-30kw-pl-300}},
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