Bubble Dynamics Transformer: Microrheology at Ultra-High Strain Rates

TL;DR

This paper introduces a machine learning framework using a neural network called Bubble Dynamics Transformer to rapidly and accurately measure the viscoelastic properties of soft biological materials at ultra-high strain rates induced by laser cavitation.

Contribution

The study develops a novel ML-based microrheological method that leverages bubble dynamics data to infer material properties at extreme strain rates, surpassing traditional techniques.

Findings

BDT accurately predicts viscoelastic parameters

Enables non-contact, high-speed material characterization

Applicable to biomedical and materials science fields

Abstract

Laser-induced inertial cavitation (LIC)-where microscale vapor bubbles nucleate due to a focused high-energy pulsed laser and then violently collapse under surrounding high local pressures-offers a unique opportunity to investigate soft biological material mechanics at extremely high strain rates (>1000 1/s). Traditional rheological tools are often limited in these regimes by loading speed, resolution, or invasiveness. Here we introduce novel machine learning (ML) based microrheological frameworks that leverage LIC to characterize the viscoelastic properties of biological materials at ultra-high strain rates. We utilize ultra-high-speed imaging to capture time-resolved bubble radius dynamics during LIC events in various soft viscoelastic materials. These bubble radius versus time measurements are then analyzed using a newly developed Bubble Dynamics Transformer (BDT), a neural network trained on physics-based simulation data. The BDT accurately infers material viscoelastic parameters, eliminating the need for iterative fitting or complex inversion processes. This enables fast, accurate, and non-contact characterization of soft materials under extreme loading conditions, with significant implications for biomedical applications and materials science.