Building high accuracy emulators for scientific simulations with deep neural architecture search · HackerPapers discussions

Building high accuracy emulators for scientific simulations with deep neural architecture search — mentioned by babel_

Thu, 28 Mar 2024 13:59:52 GMT

So, from the perspective I have within the subfield I work in, explainable AI (XAI), we're seeing a bunch of fascinating developments. First, as you mentioned, Rudin continues to prove that the reason for using AI/ML is that we don't understand the problem well enough; otherwise we wouldn't even think to use it! So, pushing our focus to better understand the problem, and then levy ML concepts and techniques (including "classical AI" and statistical learning), we're able to make something that not only outperforms some state-of-the-art in most metrics, but often even is much less resource…

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Building high accuracy emulators for scientific simulations with deep neural architecture search — mentioned by T-A

Wed, 15 Nov 2023 18:21:09 GMT

https://www.science.org/content/article/models-galaxies-atom... https://towardsdatascience.com/physics-informed-neural-netwo... https://maziarraissi.github.io/PINNs/ https://arxiv.org/abs/2001.08055 https://arxiv.org/abs/2009.11990

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Building high accuracy emulators for scientific simulations with deep neural architecture search — mentioned by Gatsky

Mon, 11 May 2020 13:52:07 GMT

Not saying that it is in any way practical or useful in the real world, but I think there are approaches which are more geared towards what we might understand as 'emulating' rather than modelling an effect. It seems that emulators can be learned from data with surprising efficiency [1]. These would be amenable to parameter control. [1] https://arxiv.org/abs/2001.08055

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Building high accuracy emulators for scientific simulations with deep neural architecture search — HN submission

Thu, 23 Jan 2020 22:17:40 GMT

Up to 2B times acceleration of scientific simulations with deep neural search

30 points; 9 discussion replies on HN. Paper and source history. A submission is not an endorsement.