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RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

open access: yesAdvanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang   +4 more
wiley   +1 more source
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The Intelligent Mine

IFAC Proceedings Volumes, 1995
Abstract Mining is commonly seen as a typical basic industry with rough and even dangerous working conditions, heavy environmental load, and a low level of high-technology and automation with much manual work and operations. A mining operation (open pit or underground) is a very complex one consisting of many manual, physical, mechanical and ...
Paavo Uronen, Raimo Matikainen
openaire   +1 more source

Data mining for intelligent Web caching

Proceedings International Conference on Information Technology: Coding and Computing, 2002
Presents a vertical application of data warehousing and data mining technology: intelligent Web caching. We introduce several ways to construct intelligent Web caching algorithms that employ predictive models of Web requests; the general idea is to extend the LRU (least recently used) policy of Web and proxy servers by making it sensible to Web access ...
Bonchi F   +6 more
openaire   +5 more sources

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