# AI-aided multiscale modeling of physiologically-significant blood clots

arXiv: https://arxiv.org/abs/2205.14121 · HTML: https://mlsanity.com/paper/2205.14121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 25 May 2022.

## Built on by (most cited first, 1 of 1)

- [Machine Learning Framework for Thrombosis Risk Prediction in Rotary Blood Pumps](https://mlsanity.com/paper/2512.15761/) (2025)

## Abstract (arXiv)

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.