33 citations · 41 across the 7 of their papers we have counts for
8 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…
Stochastic ion emission perturbation mechanisms in atom probe tomography: Linking simulations to experiment
Aslam Shaikh, Tero Mäkinen, François Vurpillot +2
Field evaporation in atom probe tomography (APT) includes known processes related to surface migration of atoms, such as the so-called roll-up mechanism. They lead to trajectory ab…
Growth and prediction of plastic strain in metallic glasses
Tero Mäkinen, Anshul D. S. Parmar, Silvia Bonfanti +1
Predicting the failure and plasticity of solids remains a longstanding challenge, with broad implications for materials design and functional reliability. Disordered solids like me…
Creep failure in heterogeneous materials from the barrier landscape
Juan Carlos Verano-Espitia, Tero Mäkinen, Mikko J. Alava +1
Stressed under a constant load, materials creep with a final acceleration of deformation and for any given applied stress and material, the creep failure time can strongly vary. We…
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…