1 citations · 1 across the 4 of their papers we have counts for
7 papers
Topological cell-openness index for porous materials
MichaÅ Bogdan, PaweÅ DÅotko
We propose a method of estimating and parametrising the proportion of open and closed cells in a porous material based on measuring Betti numbers on the structures. We define a cel…
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…
Understanding the geometry of deep learning with decision boundary volume
Matthew Burfitt, Jacek Brodzki, Pawel DÅotko
For classification tasks, the performance of a deep neural network is determined by the structure of its decision boundary, whose geometry directly affects essential properties of…
Leveraging topological data analysis to estimate bone strength from micro-CT as a surrogate for advanced imaging
John Rick Manzanares, Richard Leslie Abel, PaweÅ DÅotko
Accurate bone strength prediction is essential for assessing fracture risk, particularly in aging populations and individuals with osteoporosis. Bone imaging has evolved from X-ray…