Systematic selection of surrogate models for nonequilibrium chemistry

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Table 2

Performance metrics across datasets and models.

Dataset	Arch	mLAE (dex)	LAE₉₉ (dex)	mRE (%)	Inference time (μs)	Params (k)	mLAE_iter (dex)	mLU_DE (dex)	mLAE_DE (dex)	UQ PCC
Primordial	MON	0.343	2.47	38.5	703 ± 77	498	0.499	0.225	0.174	0.531
	FCNN	0.094	0.693	11.8	307 ± 38	896	0.273	0.154	0.101	0.581
	LNODE	0.661	3.04	80.6	18 190 ± 960	1418	1.26	0.251	0.686	0.267
	LP	0.843	3.35	90.8	619 ± 78	461	1.24	0.301	0.833	0.263
Primordial	MON	0.613	5.96	41.6	2030 ± 90	86	0.667	0.431	0.251	0.695

Parametric	FCNN	0.259	1.74	33.4	642 ± 42	454	0.719	0.172	0.121	0.512
	LNODE	0.693	3.32	81.7	41520 ± 2360	409	1.58	0.821	0.783	0.278
	LP	0.743	3.43	85.4	1040 ± 120	27	1.60	0.488	0.805	0.425

Cloud	MON	0.034	0.326	4.1	13 010 ± 10 910	459	0.187	0.031	0.024	0.654
	FCNN	0.035	0.384	3.5	814 ± 98	24	0.246	0.111	0.054	0.796
	LNODE	1.92	4.55	100.0	43 430 ± 610	286	1.92	0.008	1.91	0.122
	LP	1.64	4.70	99.9	1910 ± 190	193	1.78	0.306	1.62	0.023

Cloud	MON	0.121	0.831	16.3	8470 ± 4090	372	0.938	0.077	0.066	0.559
Parametric	FCNN	0.244	2.80	15.0	1510 ± 130	361	0.441	0.144	0.083	0.82
	LNODE	1.51	4.80	99.7	49 070 ± 520	234	1.85	0.197	1.49	-=0.017
	LP	1.37	4.51	99.7	1820 ± 120	17	2.04	0.679	1.40	0.051

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