Air pollution (25-species)

ADVANTAGES3 · dim 20

SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →

25-reaction atmospheric chemistry; large stiff system

Chemistry & reaction kinetics

Problem definition

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.

Show canonical RHS excerpt
def _pollution_rhs(t, y):
    k = np.array([
        0.35e0, 0.266e2, 0.123e5, 0.86e-3, 0.82e-3,
        0.15e5, 0.13e-3, 0.24e5, 0.165e5, 0.9e4,
        0.22e-1, 0.12e5, 0.188e1, 0.163e5, 0.48e7,
        0.35e-3, 0.175e-1, 0.1e9, 0.444e12, 0.124e4,
        0.21e1, 0.578e1, 0.474e-1, 0.178e4, 0.312e1,
    ])
    r = np.zeros(25)
    r[0] = k[0] * y[0]
    r[1] = k[1] * y[1] * y[3]
    r[2] = k[2] * y[4] * y[1]
    r[3] = k[3] * y[6]
    r[4] = k[4] * y[6]
    r[5] = k[5] * y[6] * y[5]
    r[6] = k[6] * y[8]
    r[7] = k[7] * y[8] * y[5]
    r[8] = k[8] * y[10] * y[1]
    r[9] = k[9] * y[10] * y[0]
    r[10] = k[10] * y[12]
    r[11] = k[11] * y[9] * y[1]
    r[12] = k[12] * y[13]
    r[13] = k[13] * y[0] * y[5]
    r[14] = k[14] * y[2]
    r[15] = k[15] * y[3]
    r[16] = k[16] * y[3]
    r[17] = k[17] * y[15]
    r[18] = k[18] * y[15]
    r[19] = k[19] * y[16] * y[5]
    r[20] = k[20] * y[18]
    r[21] = k[21] * y[18]
    r[22] = k[22] * y[0] * y[3]
    r[23] = k[23] * y[18] * y[0]
    r[24] = k[24] * y[19]

    dy = np.zeros(20)
    dy[0] = -r[0] - r[9] - r[13] - r[22] - r[23] + r[1] + r[2] + r[8] + r[10] + r[11] + r[21] + r[24]
    dy[1] = -r[1] - r[2] - r[8] - r[11] + r[0] + r[20]
    dy[2] = -r[14] + r[0] + r[16] + r[18] + r[21]
    dy[3] = -r[1] - r[15] - r[16] - r[22] + r[14]
    dy[4] = -r[2] + 2*r[3] + r[5] + r[6] + r[12] + r[19]
    dy[5] = -r[5] - r[7] - r[13] - r[19] + r[2] + 2*r[17]
    dy[6] = -r[3] - r[4] - r[5] + r[12]
    dy[7] = r[3] + r[4] + r[5] + r[6]
    dy[8] = -r[6] - r[7]
    dy[9] = -r[11] + r[6] + r[8]
    dy[10] = -r[8] - r[9] + r[7] + r[10]
    dy[11] = r[8]
    dy[12] = -r[10] - r[11] + r[9]
    dy[13] = -r[12] + r[11]
    dy[14] = r[12]
    dy[15] = -r[17] - r[18] + r[15]
    dy[16] = -r[19] + r[17]
    dy[17] = r[19]
    dy[18] = -r[20] - r[21] - r[23] + r[22] + r[24]
    dy[19] = -r[24] + r[23]
    return dy
Parameters
  • No captured parameters; constants are explicit in the RHS excerpt.
Initial condition
y(0) = [0, 0.2, 0, 0.04, 0, 0, …] [shape=(20,), min=0, max=0.3]
Horizon
t ∈ [0, 60]

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: medium

Recommendation snapshot

Clean best: SolvSRK

Noisy best: SolvSRK

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: Air pollution (25-species) (air-pollution-25-species)

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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
10.794011 ms0.874
2SciPy RadauSciPy
100%
8.997127 ms0.831
3SciPy BDFSciPy
100%
7.050819 ms0.787
4CVODE Adamsexternal
100%
6.616,113338 ms0.776
5CVODE BDFexternal
100%
6.640215 ms0.775
SciPy RK45SciPy
0%
SciPy LSODASciPy
0%
SciPy DOP853SciPy
0%
SciPy RK23SciPy
0%
Tsit5external
0%

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 →

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SolvSRK · 30-day trial

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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_air_pollution_25_species_2026,
  title        = {Resonix Evidence Portal: Air pollution (25-species)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/air-pollution-25-species}},
  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.}
}

Related

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