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IR Target/Background Symmetric Thermal (dim=12)

PARITYS1 · dim 12

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 →

Coupled target-background thermal evolution for IR detection. Target states (6): T_surface, T_core, emissivity, convection, radiation_flux, contrast_signal. Background states (6): identical structure with ambient parameters. Both subsystems share solver drift characteristics. Output metric: target-background contrast ratio for TOST non-inferiority (delta=5%).

Thermal & heat transfer

Problem definition

Optica (2012) thermal drift compensation for microbolometers; arXiv 2512.15211 TBC metric

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.

Show canonical RHS excerpt

def _ir_thermal_symmetric_rhs(t: float, y: np.ndarray) -> np.ndarray:
    dy = np.zeros(12)

    T_s_tgt = max(y[0], 200.0)
    T_c_tgt = max(y[1], 200.0)
    eps_tgt = np.clip(y[2], 0.01, 1.0)
    h_tgt = max(y[3], 0.1)
    q_rad_tgt = y[4]
    contrast = y[5]

    T_s_bg = max(y[6], 200.0)
    T_c_bg = max(y[7], 200.0)
    eps_bg = np.clip(y[8], 0.01, 1.0)
    h_bg = max(y[9], 0.1)
    q_rad_bg = y[10]

    rho_c_tgt = 2.5e6
    k_tgt = 15.0
    L_tgt = 0.05
    A_tgt = 0.1
    Q_int = 500.0

    q_conv_tgt = h_tgt * A_tgt * (T_s_tgt - _T_AMB)
    q_rad_actual_tgt = eps_tgt * _STEFAN_BOLTZMANN * A_tgt * (T_s_tgt**4 - _T_AMB**4)
    q_cond_tgt = k_tgt * A_tgt * (T_c_tgt - T_s_tgt) / L_tgt

    vol_tgt = A_tgt * L_tgt
    cap_surf_tgt = rho_c_tgt * vol_tgt * 0.3
    cap_core_tgt = rho_c_tgt * vol_tgt * 0.7

    dy[0] = (q_cond_tgt - q_conv_tgt - q_rad_actual_tgt) / cap_surf_tgt
    dy[1] = (Q_int - q_cond_tgt) / cap_core_tgt

    tau_eps = 2.0
    eps_eq = 0.85 + 0.05 * np.sin(0.5 * t)
    dy[2] = (eps_eq - eps_tgt) / tau_eps

    tau_h = 1.0
    h_eq_tgt = 10.0 + 2.0 * np.sin(0.3 * t)
    dy[3] = (h_eq_tgt - h_tgt) / tau_h

    dy[4] = (q_rad_actual_tgt - q_rad_tgt) / 0.1

    rho_c_bg = 1.8e6
    k_bg = 0.6
    L_bg = 0.1
    A_bg = 1.0

    q_conv_bg = h_bg * A_bg * (T_s_bg - _T_AMB)
    q_rad_actual_bg = eps_bg * _STEFAN_BOLTZMANN * A_bg * (T_s_bg**4 - _T_AMB**4)
    q_cond_bg = k_bg * A_bg * (T_c_bg - T_s_bg) / L_bg

    q_solar = 200.0 * max(0.0, np.sin(0.5 * t))

    vol_bg = A_bg * L_bg
    cap_surf_bg = rho_c_bg * vol_bg * 0.3
    cap_core_bg = rho_c_bg * vol_bg * 0.7

    dy[6] = (q_cond_bg + q_solar * A_bg * eps_bg - q_conv_bg - q_rad_actual_bg) / cap_surf_bg
    dy[7] = (-q_cond_bg) / cap_core_bg

    tau_eps_bg = 5.0
    eps_eq_bg = 0.92 + 0.02 * np.cos(0.3 * t)
    dy[8] = (eps_eq_bg - eps_bg) / tau_eps_bg

    h_eq_bg = 5.0 + 1.0 * np.cos(0.2 * t)
    dy[9] = (h_eq_bg - h_bg) / tau_h

    dy[10] = (q_rad_actual_bg - q_rad_bg) / 0.1

    radiance_tgt = eps_tgt * _STEFAN_BOLTZMANN * T_s_tgt**4
    radiance_bg = eps_bg * _STEFAN_BOLTZMANN * T_s_bg**4
    target_contrast = (radiance_tgt - radiance_bg) / max(radiance_bg, 1e-10)

    tau_contrast = 0.05
    dy[5] = (target_contrast - contrast) / tau_contrast

    dy[11] = 0.0

    return dy

Parameters: _STEFAN_BOLTZMANN = 5.67037e-08
_T_AMB = 293
Initial condition: y(0) = [350, 380, 0.85, 10, 0, 0, 293, 295, 0.92, 5, 0, 0]
Horizon: t ∈ [0, 10]

Canonical 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: SolvSRK

Noisy best: SciPy LSODA

Coverage

14 solver arms · clean + 5 noise levels

Ranked on survival, precision, and speed

Versions & freeze

Methodology →

Freeze: 2026-08-13
libsolvsrk: 2.3.0
SciPy: 1.14
SUNDIALS: CVODE (bundled backend)

20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: IR Target/Background Symmetric Thermal (dim=12) (ir-target-background-symmetric-thermal-dim-12)

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

Results matrix

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%	11.5	1,260	51 ms	0.892
2	Vern7external	100%	10.7	1,102	4.05 s	0.875
3	SciPy RadauSciPy	100%	10.1	1,399	43 ms	0.860
4	SciPy DOP853SciPy	100%	10.0	722	12 ms	0.857
5	SciPy RK45SciPy	100%	9.9	932	17 ms	0.855
6	Vern9external	100%	9.5	1,730	4.05 s	0.844
7	SciPy LSODASciPy	100%	9.3	651	9 ms	0.840
8	CVODE BDFexternal	100%	9.1	346	24 ms	0.836
9	SciPy BDFSciPy	100%	8.6	628	26 ms	0.825
10	Tsit5external	100%	8.5	804	757 ms	0.821
11	CVODE Adamsexternal	100%	8.3	299	21 ms	0.818
12	SciPy RK23SciPy	100%	8.2	1,553	30 ms	0.813
13	FBDFexternal	100%	7.8	441	5.31 s	0.806
14	TRBDF2external	100%	6.4	1,005	5.22 s	0.771

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 →

Cite this page

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_ir_target_background_symmetric_thermal_dim_12_2026,
  title        = {Resonix Evidence Portal: IR Target/Background Symmetric Thermal (dim=12)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonixusa.com/evidence/problems/ir-target-background-symmetric-thermal-dim-12}},
  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

Problem definition

Versions & freeze

Results matrix

Cite this page

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