paper: doi:10.1016/j.cma.2019.112732

Ingested works. lom_id is the stable key (doi: or arxiv:). OpenAlex/S2/arXiv ids when found; never fabricated.

Data license: Space charter; records cite primary sources · Data source: TeamScience Space repository

This data as json

lom_id	doi	openalex	s2_paper_id	arxiv	pmid	pmcid	acl	title	year	venue	oa_url	ingested_ts	source
doi:10.1016/j.cma.2019.112732	10.1016/j.cma.2019.112732	W2948230027						Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data	2019	Computer Methods in Applied Mechanics and Engineering	https://arxiv.org/pdf/1906.02382	2026-09-02T02:37:09Z	openalex

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