57 citations · 257 across the 17 of their papers we have counts for
20 papers
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…
Crack propagation by activated avalanches during creep and fatigue from elastic interface theory
Tero Mäkinen, Lumi Tuokkola, Joonas Lahikainen +3
The growth of cracks combines materials science, fracture mechanics, and statistical physics. The importance of fluctuations in the crack velocity is fundamental since it signals t…
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…
Striation lines in intermittent fatigue crack growth in an Al alloy
Anniina Kinnunen, Ivan V. Lomakin, Tero Mäkinen +3
Fatigue failure of crystalline materials is a difficult problem in science and engineering, and recent results have shown that fatigue crack growth can occur in intermittent jumps…
Wood compression in four-dimensional in situ tomography
Tero Mäkinen, Alisa Halonen, Juha Koivisto +1
Wood deformation, in particular when subject to compression, exhibits scale-free avalanche-like behavior as well as structure-dependent localization of deformation. We have taken t…