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C.29:13a - References

The comparison above selects the first-use Method. The references below provide further source returns for the particular discovery cues and model conditions in :4.2b/:4.5a; they do not rank those families for a working problem.

SourceLocator
SAND-THREAD-MATH-LINKS-2026-05-12Links for 2026-05-12, Math section
VAN-GEOM-LEARNING-2025/2026Geometric Learning Dynamics, v3, 14 March 2026
RODIN-2023https://arxiv.org/abs/2301.08131
FONG-SPIVAK-2018/2019https://arxiv.org/abs/1803.05316; Cambridge page: https://www.cambridge.org/core/books/an-invitation-to-applied-category-theory/D4C5E5C2B019B2F9B8CE9A4E9E84D6BC
GDL-BRONSTEIN-2021https://arxiv.org/abs/2104.13478
PEYRE-CUTURI-2019https://arxiv.org/abs/1803.00567
PUCA-ETAL-2023https://arxiv.org/abs/2307.14461
MODEL-CARDS-2018/2019https://arxiv.org/abs/1810.03993
DATASHEETS-2018/2021https://arxiv.org/abs/1803.09010; CACM page: https://cacm.acm.org/research/datasheets-for-datasets/
CAUSAL-CONSISTENCY-2017https://arxiv.org/abs/1707.00819
CAUSAL-ABSTRACTION-2019https://arxiv.org/abs/1812.03789; AAAI page: https://ojs.aaai.org/index.php/AAAI/article/view/4117
APPROX-CAUSAL-ABSTRACTION-2019/2020https://arxiv.org/abs/1906.11583; PMLR page: https://proceedings.mlr.press/v115/beckers20a.html
CAUSAL-ABSTRACTION-JMLR-2025https://jmlr.org/beta/papers/v26/23-0058.html
SCHOLKOPF-ETAL-2021https://arxiv.org/abs/2102.11107; DOI 10.1109/JPROC.2021.3058954
PINN-2019DOI 10.1016/j.jcp.2018.10.045
PIML-2021DOI 10.1038/s42254-021-00314-5
DEEPONET-2021DOI 10.1038/s42256-021-00302-5
FNO-2020/2021https://arxiv.org/abs/2010.08895
SCIML-DIETRICH-SCHILDERS-2025DOI 10.1007/s00591-025-00399-4; https://link.springer.com/article/10.1007/s00591-025-00399-4
PIML-SURVEY-2025DOI 10.1007/s44379-025-00016-0; https://link.springer.com/article/10.1007/s44379-025-00016-0
NEURAL-OPERATORS-NRP-2024DOI 10.1038/s42254-024-00712-5; https://www.nature.com/articles/s42254-024-00712-5
PHYSICS-FOUNDATION-MODEL-2025https://arxiv.org/abs/2509.13805
KOOPMAN-SINDY-DMD-2016SINDy DOI 10.1073/pnas.1517384113; DMD DOI 10.1137/1.9781611974508
BAYES-WORKFLOW-PPL-2018/2020Probabilistic programming arXiv https://arxiv.org/abs/1809.10756; Bayesian Workflow arXiv https://arxiv.org/abs/2011.01808
MODERN-BED-2023/2024https://arxiv.org/abs/2302.14545; DOI 10.48550/arXiv.2302.14545
MODERN-OED-2024/2026https://arxiv.org/abs/2407.16212; Cambridge Core DOI 10.1017/S0962492924000023
BO-AL-ADAPTIVE-SAMPLING-2024DOI 10.1007/s11831-024-10064-z; https://link.springer.com/article/10.1007/s11831-024-10064-z
EIG-DENSITY-APPROX-2024/2026https://arxiv.org/abs/2411.08390; DOI 10.48550/arXiv.2411.08390
ROBUST-GBOED-2025https://arxiv.org/abs/2511.07671; DOI 10.48550/arXiv.2511.07671
OBERKAMPF-ROY-2010Cambridge page: https://www.cambridge.org/core/books/verification-and-validation-in-scientific-computing/contents/9399D588DE8B3D49E392CF0436D5A67D
NRC-VVUQ-2012DOI 10.17226/13395; https://nap.nationalacademies.org/catalog/13395/assessing-the-reliability-of-complex-models-mathematical-and-statistical-foundations
GNEITING-RAFTERY-2007DOI 10.1198/016214506000001437