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12.02.2022 | Letter to the Editor

Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox and Causal Artificial Intelligence

verfasst von: M. Z. Naser

Erschienen in: Fire Technology | Ausgabe 3/2022

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Dear Editor, …

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This letter is inspired in part by Prof. TZ Harmathy’s work “Ten rules of fire endurance rating” [32] (also published at Fire Technology).

This is an important point—especially since many schools with fire engineering courses may not have experimental facilities. Sharing our data and codes will allow less representative schools to be active members of our domain—simply since AI investigation can be carried out by freely available codes (yet, our data may not be freely available)—see Big Idea 9.

For fairness, new knowledge could also be discovered via data-driven models (but not to the same extent as that in causalmodels).

One can argue that real data from tests may not be representative with real work observations (given the limitation of testing set-ups). This is a valid argument. The second paragraph of Rule 3 may counter such argument.

One can think of a numerical model where dense mesh is used vs. low quality mesh. A trade-off on the front of accuracy-computation resources is expected to exist.

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Titel: Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox and Causal Artificial Intelligence
verfasst von: M. Z. Naser
Publikationsdatum: 12.02.2022
Verlag: Springer US
Erschienen in: Fire Technology / Ausgabe 3/2022
Print ISSN: 0015-2684
Elektronische ISSN: 1572-8099
DOI: https://doi.org/10.1007/s10694-021-01210-1

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