Výsledky vyhledávání

Report

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Autor: Chung, Wai Tong, Akoush, Bassem, Sharma, Pushan, Tamkin, Alex, Jung, Ki Sung, Chen, Jacqueline H., Guo, Jack, Brouzet, Davy, Talei, Mohsen, Savard, Bruno, Poludnenko, Alexei Y., Ihme, Matthias

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity

Externí odkaz: http://arxiv.org/abs/2309.13457

Zobrazit plný text záznamu

Akademický článek

BLASTNet: A call for community-involved big data in combustion machine learning

Autor: Chung, Wai Tong, Jung, Ki Sung, Chen, Jacqueline H., Ihme, Matthias

Publikováno v: In Applications in Energy and Combustion Science December 2022 12

Zobrazit plný text záznamu

Akademický článek

BLASTNet: A call for community-involved big data in combustion machine learning

Autor: Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme

Publikováno v: Applications in Energy and Combustion Science, Vol 12, Iss , Pp 100087- (2022)

Many state-of-the-art machine learning (ML) fields rely on large datasets and massive deep learning models (with O(109) trainable parameters) to predict target variables accurately without overfitting. Within combustion, a wealth of data exists in th

Externí odkaz: https://doaj.org/article/abb66e0b52ed4056a8616fe487e2a0c2

Zobrazit plný text záznamu