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1.
Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations / Rose, Jonah C. (Florida U. ; New York U., CCPP) ; Torrey, Paul (Virginia U.) ; Villaescusa-Navarro, Francisco (New York U., CCPP ; Princeton U., Astrophys. Sci. Dept.) ; Lisanti, Mariangela (New York U., CCPP ; Princeton U.) ; Nguyen, Tri (MIT, MKI ; IAIFI, Cambridge) ; Roy, Sandip (Princeton U.) ; Kollmann, Kassidy E. (Princeton U.) ; Vogelsberger, Mark (MIT, MKI ; IAIFI, Cambridge) ; Cyr-Racine, Francis-Yan (New Mexico U.) ; Medvedev, Mikhail V. (Princeton U., Astrophys. Sci. Dept. ; Princeton, Inst. Advanced Study ; Harvard-Smithsonian Ctr. Astrophys. ; Kansas U. ; MIT, LNS) et al.
We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. [...]
arXiv:2405.00766.
- 28 p.
Fulltext
2.
Applications and Techniques for Fast Machine Learning in Science / Deiana, Allison McCarn (Southern Methodist U.) ; Tran, Nhan (Fermilab ; Northwestern U. (main)) ; Agar, Joshua (Lehigh U. (main)) ; Blott, Michaela (Xilinx, Dublin) ; Di Guglielmo, Giuseppe (Columbia U. (main)) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT) ; Hauck, Scott (George Washington U. (main)) ; Liu, Mia (Purdue U.) ; Neubauer, Mark S. (Illinois U., Urbana) et al.
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. [...]
arXiv:2110.13041; FERMILAB-PUB-21-502-AD-E-SCD.- 2022-04-12 - 56 p. - Published in : Front. Big Data 5 (2022) 787421 Fulltext: 2110.13041 - PDF; fermilab-pub-21-502-ad-e-scd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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