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Hydrodynamics

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Machine learning active-nematic hydrodynamics.

Proceedings of the National Academy of Sciences of the United States of America
Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in...

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.

mAbs
High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein interactions among many antibodies. Molecular simulation is a promising method to study protein-protein ...

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems.

Big data
Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their hi...

Elucidating the interaction dynamics between microswimmer body and immune system for medical microrobots.

Science robotics
The structural design parameters of a medical microrobot, such as the morphology and surface chemistry, should aim to minimize any physical interactions with the cells of the immune system. However, the same surface-borne design parameters are also c...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Neurosurgical focus
OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD trea...