AI-aided multiscale modeling of physiologically-significant blood clots
This work addresses the problem of modeling complex blood clotting processes for biomedical research, representing a novel method rather than an incremental improvement.
The researchers tackled the challenge of simulating physiologically-significant blood clots by developing an AI-aided multiscale modeling framework that integrates multi-physics interactions, achieving a record-setting simulation of 102 million particles with 70 flowing and 180 aggregating platelets.
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.