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O se tasi o le sili ona taua talosaga o le masini masini aʻoaʻoga i le prometional physics o le vave fofo o vaega o vaega (pā). O le sini autu o le masini aʻoaʻoga vaega vaega o le saʻo o loʻo maua ai fofo e sili atu le saʻo nai lo metotia faʻavae faʻatulagaina. Matou te faia muamua se iloiloga o le masini faʻataʻitaʻi o le masini aʻoaʻoga tusitusiga i le fofoina o vaega eseese. O pepa uma na lipotia le faʻaaogaina o ML e foia ai le tele o vaega o le vaega o vaega ma le manaʻo i le sili atu numera faʻasologa, matou faʻailoa 79% (60/76)) faʻatusatusa i le vaivai fua o le platines. Lona lua, ua matou mauaina foi faʻamaoniga o faʻasalalauga faʻafitafita faʻaituau, aemaise lava i le iʻuga o le lipoti ma le lomia Faʻamasino. We conclude that machine learning research on solving partial differential equations is overly optimistic: weak input data can lead to overly positive results, and reporting bias can lead to underreporting of negative results. In large part, these problems appear to be caused by factors similar to past reproducibility crises: investigator discretion and positive outcome bias. We call for bottom-up cultural change to minimize biased reporting and top-down structural reform to reduce perverse incentives to do so.
O le lisi o tusitala ma tusitusiga na faia e le iloiloga o iloiloga, faʻapea foi ma le faʻavasegaina o tusitusiga taʻitasi i le soʻotaga taʻitasi, e avanoa lautele i HTTP:1.17605/gosF.4).
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Taimi o le taimi: Setema-29-2024