| Autor: |
Zhu, Yicong, Han, Changnian, Zhang, Peng, Cong, Guojing, Kozloski, James R., Yang, Chih-Chieh, Zhang, Leili, Deng, Yuefan |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.cpc.2023.108718 |
| Popis: |
We have developed an AI-aided multiple time stepping (AI-MTS) algorithm and multiscale modeling framework (AI-MSM) and implemented them on the Summit-like supercomputer, AIMOS. AI-MSM is the first of its kind to integrate multi-physics, including intra-platelet, inter-platelet, and fluid-platelet interactions, into one system. It has simulated a record-setting multiscale blood clotting model of 102 million particles, of which 70 flowing and 180 aggregating platelets, under dissipative particle dynamics to coarse-grained molecular dynamics. By adaptively adjusting timestep sizes to match the characteristic time scales of the underlying dynamics, AI-MTS optimally balances speeds and accuracies of the simulations. |
| Databáze: |
arXiv |
| Externí odkaz: |
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