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ORCIDAI4S@SC 2022: Dallas, TX, USA
- IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S@SC 2022, Dallas, TX, USA, November 13-18, 2022. IEEE 2022, ISBN 978-1-6654-6207-5
- Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka:
Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging. 1-6 - Fabian Orland
, Kim Sebastian Brose, Julian Bissantz, Federica Ferraro, Christian Terboven, Christian Hasse:
A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks. 7-12 - Rajat Arora:
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics. 13-18 - Nadav Schneider, Matan Rusanovsky, Raz Gvishi, Gal Oren:
Determining HEDP Foams' Quality with Multi-View Deep Learning Classification. 19-25 - Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz, Anna Sikora, Eduardo César, Ali Jannesari:
Pattern-based Autotuning of OpenMP Loops using Graph Neural Networks. 26-31 - Hana Ahmed, Roselyne Tchoua, Jay F. Lofstead:
Ensuring AI For Science is Science: Making Randomness Portable. 32-37 - Lesi Wang, Dongfang Zhao:
Practical Federated Learning Infrastructure for Privacy-Preserving Scientific Computing. 38-43 - Mathew Boyer, Wesley Brewer, Dylan Jude, Ian D. Dettwiller:
Scalable Integration of Computational Physics Simulations with Machine Learning. 44-49
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