Marcin Mińkowski

Deep learning-based surrogate model for generation of wood microstructures

Marcin Mińkowski, Fahime Seyedheydari, Mikko Seppi, Bin Chen et autres

A machine learning-based surrogate model for efficient wood microstructure generation compatible with a physics-based model is developed. The model is based on the U-Net neural network, a variant of convolutional neural network which, due to its architecture, is suitable for image-to-image transformation, …

fi, se, ch (code pays fourni par la source)

Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression

Fahime Seyedheydari, Mahdi Nasiri, Marcin Mińkowski, Simo Särkkä

In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly formulated as a Fredholm integral equation of the first kind, an …

Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning

Marcin Mińkowski, Lasse Laurson

Machine learning models have proven to be powerful tools to discover links between microstructure and properties of materials, but the black box nature of the models limits the physical insights one might gain from them. Here, we study the relationship between the …

fi, us (code pays fourni par la source)

Estimating predictability of depinning dynamics by machine learning

Valtteri Haavisto, Marcin Mińkowski, Lasse Laurson

Abstract Predicting the future behavior of complex systems exhibiting critical-like dynamics is often considered to be an intrinsically hard task. Here, we study the predictability of the depinning dynamics of elastic interfaces in random media driven by a slowly increasing external force, …

fi (code pays fourni par la source)

Estimating predictability of depinning dynamics by machine learning

Valtteri Haavisto, Marcin Mińkowski, Lasse Laurson

Predicting the future behaviour of complex systems exhibiting critical-like dynamics is often considered to be an intrinsically hard task. Here, we study the predictability of the depinning dynamics of elastic interfaces in random media driven by a slowly increasing external force, a …

Predicting elastic and plastic properties of small iron polycrystals by machine learning

Marcin Mińkowski, Lasse Laurson

Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the …

fi (code pays fourni par la source)

Predicting elastic and plastic properties of small iron polycrystals by machine learning

Marcin Mińkowski, Lasse Laurson

Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the …

fi (code pays fourni par la source)

Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity

Marcin Mińkowski, David Kurunczi-Papp, Lasse Laurson

Predicting the behavior of complex systems is one of the main goals of science. An important example is plastic deformation of micron-scale crystals, a process mediated by collective dynamics of dislocations, manifested as broadly distributed strain bursts and significant sample-to-sample variations in …

fi (code pays fourni par la source)

Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity

Marcin Mińkowski, David Kurunczi-Papp, Lasse Laurson

Predicting the behaviour of complex systems is one of the main goals of science. An important example is plastic deformation of micron-scale crystals, a process mediated by collective dynamics of dislocations, manifested as broadly distributed strain bursts and significant sample-to-sample variations in …

Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulations

Marcin Mińkowski, Kerstin Hummer, Christoph Dellago

Using a recently developed approach to represent ab initio based force fields by a neural network potential, we perform molecular dynamics simulations of lead telluride and cadmium telluride crystals. In particular, we study the diffusion of a single cation interstitial in these …

at (code pays fourni par la source)

Collective diffusion of dense adsorbate at surfaces of arbitrary geometry

Marcin Mińkowski, Magdalena A. Załuska–Kotur

Abstract A convenient variational formula for collective diffusion of many particles adsorbed at lattices of arbitrary geometry is formulated. The approach allows us to find the expressions for the diffusion coefficient for any value of the system’s coverage. It is assumed that …

pl (code pays fourni par la source)

Self-organization process in crystalline PbTe/CdTe multilayer structures: Experiment and Monte Carlo simulations

Marcin Mińkowski, Magdalena A. Załuska–Kotur, Sławomir Kret, S. Chusnutdinow et autres

pl, de (code pays fourni par la source)