23 citations · 60 across the 36 of their papers we have counts for
9 papers · 1 filter
Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
Marko Petković, Vlado Menkovski, Sofía Calero
Automated characterization of porous materials has the potential to accelerate materials discovery, but it remains limited by the complexity of simulation setup and force field sel…
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…
Score Matching on Large Geometric Graphs for Cosmology Generation
Diana-Alexandra Onutu, Yue Zhao, Joaquin Vanschoren +1
Generative models are a promising tool to produce cosmological simulations but face significant challenges in scalability, physical consistency, and adherence to domain symmetries,…
Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses
Fleur Hendriks, Vlado Menkovski, Martin Doškář +3
Mechanical metamaterials often exhibit pattern transformations through instabilities, enabling applications in, e.g., soft robotics, sound reduction, and biomedicine. These transfo…
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…
Plasma State Monitoring and Disruption Characterization using Multimodal VAEs
Yoeri Poels, Alessandro Pau, Christian Donner +6
When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and mag…
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
Marko Petković, José-Manuel Vicent Luna, El\=ıza Beate Dinne +2
Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task. This challenge becomes even more pronounced when attempti…
Robust Confinement State Classification with Uncertainty Quantification through Ensembled Data-Driven Methods
Yoeri Poels, Cristina Venturini, Alessandro Pau +4
Maximizing fusion performance in tokamaks relies on high energy confinement, often achieved through distinct operating regimes. The automated labeling of these confinement states i…
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…