2 citations · 4 across the 14 of their papers we have counts for
9 papers · 1 filter
BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces
Rachna Ramesh, Kiet Bennema ten Brinke, Douwe Orij +2
Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological…
Equivariant Flow Matching for Symmetry-Breaking Bifurcation Problems
Fleur Hendriks, Ondřej Rokoš, Martin Doškář +2
Bifurcation phenomena in nonlinear dynamical systems often lead to multiple coexisting stable solutions, particularly in the presence of symmetry breaking. Deterministic machine le…
STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
Kiet Bennema ten Brinke, Koen Minartz, Vlado Menkovski
Simulating trajectories of dynamical systems is a fundamental problem in a wide range of fields such as molecular dynamics, biochemistry, and pedestrian dynamics. Machine learning…
Deep Neural Cellular Potts Models
Koen Minartz, Tim d'Hondt, Leon Hillmann +3
The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics…
Topological degree as a discrete diagnostic for disentanglement, with applications to the VAE
Mahefa Ratsisetraina Ravelonanosy, Vlado Menkovski, Jacobus W. Portegies
We investigate the ability of Diffusion Variational Autoencoder (VAE) with unit sphere as latent space to capture topological and geometrical structure and disen…
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
Pol Timmer, Koen Minartz, Vlado Menkovski
Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science an…