arXiv subjects

Mia Morrell

Publications and source records attributed to Mia Morrell.

3 recordsLinked to original sources

arXiv2026

A minimal mechanism to generate long timescales without fine tuning

Long timescales in brain dynamics give rise to power law correlations measured in experiments. A simple linear recurrent neural network model can reproduce this power law behavior, but the recurrent interaction strength needs to be fine tuned in order to sit at the edge of stability. We show that by adding a dynamical recurrent feedback gain to the simple linear model removes the need for fine tuning, because the gain self-organizes so that the spectrum of the effective recurrent matrix is always gapless with the top eigenvalue landing on the stability edge for any recurrent strength. As a consequence, long timescales and scale free correlations arise generically, as we demonstrate both analytically and numerically.

cond-mat.dis-nn↗

arXiv2020

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics to extract flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. We compare the performance of three different BCNN models. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network derives velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full test image pair, showing that 100% of true particle displacements can be captured within its 95% confidence interval. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. To our knowledge, this is the first effort to use Bayesian neural networks to perform particle image velocimetry.

physics.flu-dyn↗

arXiv2015

Clogging of soft particles in 2D hoppers

Using experiments and simulations, we study the flow of soft particles through quasi-two-dimensional hoppers. The first experiment uses oil-in-water emulsion droplets in a thin sample chamber. Due to surfactants coating the droplets, they easily slide past each other, approximating soft frictionless disks. For these droplets, clogging at the hopper exit requires a narrow hopper opening only slightly larger than the droplet diameter. The second experiments use soft hydrogel particles in a thin sample chamber, where we vary gravity by changing the tilt angle of the chamber. For reduced gravity, clogging becomes easier, and can occur for larger hopper openings. Our simulations mimic the emulsion experiments and demonstrate that softness is a key factor controlling clogging: with stiffer particles or a weaker gravitational force, clogging is easier. The fractional amount a single particle is deformed under its own weight is a useful parameter measuring particle softness. Data from the simulation and hydrogel experiments collapse when compared using this parameter. Our results suggest that prior studies using hard particles were in a limit where the role of softness is negligible which causes clogging to occur with significantly larger openings.

cond-mat.soft↗