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4 papers
Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
Sergei Zorkaltsev, RafaÅ Topolnicki, Tal-El Carmon +4
The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated th…
Physics-informed convolutional neural networks for fluid flow through porous media
RafaÅ Topolnicki, PaweÅ DÅotko, Maciej Matyka
Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-Stokes equations. This diffic…
Direction-aware topological descriptors for Young's modulus prediction in porous materials
RafaÅ Topolnicki, MichaÅ Bogdan, Jakub Malinowski +3
Classical topological descriptors used in topological data analysis (TDA) are invariant under permutations of spatial axes and therefore cannot represent the loading direction, whi…
Reducing Estimation Uncertainty Using Normalizing Flows and Stratification
PaweÅ Lorek, RafaÅ Nowak, RafaÅ Topolnicki +3
Estimating the expectation of a real-valued function of a random variable from sample data is a critical aspect of statistical analysis, with far-reaching implications in various a…