23 citations · 60 across the 36 of their papers we have counts for
38 papers
RaspAssistant as an Agentic Framework for Automating Adsorption Simulation Research in Porous Materials
Marko Petković, Susanne Gooijer, José Manuel Vicent-Luna +2
Porous-material adsorption studies require coordinated structure preparation, force-field selection, simulation, validation, and interpretation, making end-to-end automation diffic…
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics
Kiet Bennema ten Brinke, Rachna Ramesh, Douwe Orij +2
Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensive. We propose a neural surr…
Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
Gabriele Gianuzzo, Stefan Dasbach, Fleur Hendriks +2
Fast surrogates for tokamak boundary-plasma simulation are typically deterministic regressors mapping a global operating point to a flattened vector of cell values. Near the divert…
A Dataset of Equilibrium State Configurations of Adsorption in Zeolites
Marko Petković, Rachna Ramesh, Vlado Menkovski +1
Zeolites are crystalline nanoporous materials widely used in adsorption, separation, and catalytic processes. Molecular simulations are commonly used to predict adsorption properti…
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…
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…
Aspect-Based Few-Shot Learning
Tim van Engeland, Lu Yin, Vlado Menkovski
We generalize the formulation of few-shot learning by introducing the concept of an aspect. In the traditional formulation of few-shot learning, there is an underlying assumption t…
Understanding complex crowd dynamics with generative neural simulators
Koen Minartz, Fleur Hendriks, Simon Martinus Koop +2
Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, bo…
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…
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…
Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials
Fleur Hendriks, Vlado Menkovski, Martin Doškář +2
Soft, porous mechanical metamaterials exhibit pattern transformations that may have important applications in soft robotics, sound reduction and biomedicine. To design these innova…
VADA: a Data-Driven Simulator for Nanopore Sequencing
Jonas Niederle, Simon Koop, Marc Pagès-Gallego +1
Nanopore sequencing offers the ability for real-time analysis of long DNA sequences at a low cost, enabling new applications such as early detection of cancer. Due to the complex n…
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski +1
The ability to efficiently predict adsorption properties of zeolites can be of large benefit in accelerating the design process of novel materials. The existing configuration space…
Description Generation using Variational Auto-Encoders for precursor microRNA
Marko Petković, Vlado Menkovski
Micro RNAs (miRNA) are a type of non-coding RNA, which are involved in gene regulation and can be associated with diseases such as cancer, cardiovascular and neurological diseases.…
Node Classification in Random Trees
Wouter W. L. Nuijten, Vlado Menkovski
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the…