AI-aided multiscale modeling of physiologically-significant blood clots

Yicong Zhu, Changnian Han, Peng Zhang, Guojing Cong, James R. Kozloski, Chih Chieh Yang, Leili Zhang, Yuefan Deng

Research output: Contribution to journalArticlepeer-review

Abstract

We have developed an AI-aided multiple time stepping (AI-MTS) algorithm and multiscale modeling framework (AI-MSM) and implemented them on the AiMOS supercomputer. 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.

Original languageEnglish
Article number108718
JournalComputer Physics Communications
Volume287
DOIs
StatePublished - Jun 2023

Funding

This project is supported by the SUNY-IBM Consortium Award, IPDyna: Intelligent Platelet Dynamics, FP00004096 (PI: Y. Deng). The simulations in this study were conducted on the AiMOS supercomputer at Rensselaer Polytechnic Institute and the SeaWulf Cluster at Stony Brook University (PIs: Y. Deng and P. Zhang). This project is supported by the SUNY-IBM Consortium Award, IPDyna: Intelligent Platelet Dynamics , FP00004096 (PI: Y. Deng). The simulations in this study were conducted on the AiMOS supercomputer at Rensselaer Polytechnic Institute and the SeaWulf Cluster at Stony Brook University (PIs: Y. Deng and P. Zhang).

Keywords

  • Artificial intelligence
  • Blood clotting
  • HPC
  • Multiscale modeling

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