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 →
12-layer [0/90]_6 carbon-fiber crossply laminate with gas pressure (dim=540, S3)
Kamal & Sourour (1973); Pojman (2012)
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 _kamal_sourour_rate_vec(T: np.ndarray, alpha: np.ndarray) -> np.ndarray:
T_safe = np.clip(T, _T_FLOOR, _T_CEIL)
alpha_safe = np.clip(alpha, 0.0, 1.0)
inv_RT = 1.0 / (_R_GAS * T_safe)
arg1 = np.clip(_E1 * inv_RT, 0.0, _EXP_ARG_MAX)
arg2 = np.clip(_E2 * inv_RT, 0.0, _EXP_ARG_MAX)
k1 = _A1 * np.exp(-arg1)
k2 = _A2 * np.exp(-arg2)
return (k1 + k2 * np.power(alpha_safe, _M)) * np.power(1.0 - alpha_safe, _N_ORD)
def _fp_crossply_12layer_rhs(t, y):
T = np.clip(y[:_CP12_N_SPATIAL], _T_FLOOR, _T_CEIL)
alpha = np.clip(y[_CP12_N_SPATIAL:2 * _CP12_N_SPATIAL], 0.0, 1.0)
P = y[2 * _CP12_N_SPATIAL:]
dadt = _kamal_sourour_rate_vec(T, alpha)
dT = np.empty(_CP12_N_SPATIAL)
dP = np.empty(_CP12_N_SPATIAL)
for layer in range(_CP12_LAYERS):
start = layer * _CP12_NODES
end = start + _CP12_NODES
diff_layer = _CP12_DIFF[layer]
for i in range(start, end):
local = i - start
if i == 0:
T_left = _CP12_T_BOTTOM
elif local == 0:
prev_layer = layer - 1
k_left = _CP12_K_THRU[prev_layer]
k_right = _CP12_K_THRU[layer]
R_left = _CP12_DX / (2.0 * k_left) + _R_CONTACT
R_right = _CP12_DX / (2.0 * k_right)
T_left = (T[i - 1] / R_left + T[i] / R_right) / (1.0 / R_left + 1.0 / R_right)
else:
T_left = T[i - 1]
if i == _CP12_N_SPATIAL - 1:
k_top = _CP12_K_THRU[layer]
T_right = T[i] + (_H_CONV * _CP12_DX / k_top) * (_T_AMBIENT - T[i])
elif local == _CP12_NODES - 1 and layer < _CP12_LAYERS - 1:
k_left_b = _CP12_K_THRU[layer]
k_right_b = _CP12_K_THRU[layer + 1]
R_left_b = _CP12_DX / (2.0 * k_left_b)
R_right_b = _CP12_DX / (2.0 * k_right_b) + _R_CONTACT
T_right = (T[i] / R_left_b + T[i + 1] / R_right_b) / (1.0 / R_left_b + 1.0 / R_right_b)
else:
T_right = T[i + 1]
lap = (T_left - 2.0 * T[i] + T_right) * _CP12_INV_DX2
dT[i] = diff_layer * lap + _SRC_COEFF * dadt[i]
dT[0] = 0.0
dP[:] = (
(_RHO_RESIN * _V_GAS_SPECIFIC * dadt * _R_GAS_IDEAL * T) / _V_PORE
- P * _PERM_LOSS
)
dy = np.empty(_CP12_DIM)
dy[:_CP12_N_SPATIAL] = dT
dy[_CP12_N_SPATIAL:2 * _CP12_N_SPATIAL] = dadt
dy[2 * _CP12_N_SPATIAL:] = dP
return dyCanonical 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: high
Recommendation snapshot
Clean best: SciPy DOP853
Noisy best: SciPy BDF
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: FP 12-Layer Crossply High-Res (fp-12-layer-crossply-high-res)
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% | 12.8 | 66,470 | 12.34 s | 0.924 |
| 2 | Tsit5external | 100% | 11.2 | 76,980 | 23.18 s | 0.884 |
| 3 | SciPy RK45SciPy | 100% | 11.0 | 74,690 | 13.90 s | 0.881 |
| 4 | SolvSRK | 100% | 11.0 | 74,683 | 13.74 s | 0.880 |
| 5 | SciPy RadauSciPy | 100% | 10.8 | 5,216 | 1.49 s | 0.875 |
| 6 | SciPy RK23SciPy | 100% | 9.3 | 42,356 | 8.03 s | 0.842 |
| 7 | SciPy LSODASciPy | 100% | 7.7 | 13,694 | 2.54 s | 0.801 |
| 8 | CVODE BDFexternal | 100% | 7.4 | 2,953 | 695 ms | 0.794 |
| 9 | SciPy BDFSciPy | 100% | 7.2 | 2,746 | 681 ms | 0.791 |
| 10 | CVODE Adamsexternal | 100% | 7.1 | 25,149 | 6.06 s | 0.788 |
At Clean, best balanced arm is SciPy DOP853 · SolvSRK survival 100%, SCD 11.0.
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_fp_12_layer_crossply_high_res_2026,
title = {Resonix Evidence Portal: FP 12-Layer Crossply High-Res},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/fp-12-layer-crossply-high-res}},
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