Bubble Dynamics Transformer: Microrheology at Ultra-High Strain Rates
Journal:
arXiv
Published Date:
Jun 13, 2025
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.
