6 citations · 7 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Yoeri Poels, Koen Minartz, Harshit Bansal +1
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations…
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Koen Minartz, Yoeri Poels, Simon Koop +1
Neural networks are emerging as a tool for scalable data-driven simulation of high-dimensional dynamical systems, especially in settings where numerical methods are infeasible or c…