57 citations · 257 across the 17 of their papers we have counts for
7 papers · 1 filter
Direct inference of viscoelastic memory from chirp rheometry via physics-informed Gaussian processes
Isaac Y. Miranda-Valdez, Juha Koivisto, Mikko J. Alava
Soft materials remember their deformation history, and identifying that memory from experiments is essential for predicting how these materials behave under real-world loading cond…
Bayesian optimization to infer parameters in viscoelasticity
Isaac Y. Miranda-Valdez, Tero Mäkinen, Juha Koivisto +1
Inferring viscoelasticity parameters is a key challenge that often leads to non-unique solutions when fitting rheological data. In this context, we propose a machine learning appro…
pyRheo: An open-source Python package for complex rheology
Isaac Y. Miranda-Valdez, Aaro Niinistö, Tero Mäkinen +3
Mathematical modeling is a powerful tool in rheology, and we present pyRheo, an open-source package for Python designed to streamline the analysis of creep, stress relaxation, osci…
Machine learning and predicting the time dependent dynamics of local yielding in dry foams
Leevi Viitanen, Jonatan R. Mac Intyre, Juha Koivisto +2
The yielding of dry foams is enabled by small elementary yield events on the bubble scale, "T1"s. We study the large scale detection of these in an expanding 2D flow geometry using…
Friction controls submerged granular flows
Juha Koivisto, Marko Korhonen, Mikko J. Alava +3
We investigate the coupling between interstitial medium and granular particles by studying the hopper flow of dry and submerged system experimentally and numerically. In accordance…
Effect of interstitial fluid on the fraction of flow microstates that precede clogging in granular hoppers
Juha Koivisto, Douglas J. Durian
We report on the nature of flow events for the gravity-driven discharge of glass beads through a hole that is small enough that the hopper is susceptible to clogging. In particular…