Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks. - Artur P. Toshev; Gianluca Galletti; Johannes Brandstetter; Stefan Adami; Nikolaus A. Adams (GSI (2), 2021)

Accelerating Molecular Graph Neural Networks via Knowledge Distillation. - Filip Ekström Kelvinius; Dimitar Georgiev; Artur P. Toshev; Johannes Gasteiger (NeurIPS, 2023)

LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite. - Artur P. Toshev; Gianluca Galletti; Fabian Fritz; Stefan Adami; Nikolaus A. Adams (NeurIPS, 2023)

On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods. - Artur P. Toshev; Ludger Paehler; Andrea Panizza; Nikolaus A. Adams (CoRR, 2023)

E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics. - Artur P. Toshev; Gianluca Galletti; Johannes Brandstetter; Stefan Adami; Nikolaus A. Adams (CoRR, 2023)

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics. - Artur P. Toshev; Jonas A. Erbesdobler; Nikolaus A. Adams; Johannes Brandstetter (ICML, 2024)

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework. - Artur P. Toshev; Harish Ramachandran; Jonas A. Erbesdobler; Gianluca Galletti; Johannes Brandstetter; Nikolaus A. Adams (CoRR, 2024)

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning. - Thorben Prein; Elton Pan; Sami Haddouti; Marco Lorenz; Janik Jehkul; Tymoteusz Wilk; Cansu Moran; Menelaos Panagiotis Fotiadis; Artur P. Toshev; Elsa A. Olivetti; Jennifer L. M. Rupp (CoRR, 2025)

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities. - Florian Sestak; Artur P. Toshev; Andreas Fürst; Günter Klambauer; Andreas Mayr; Johannes Brandstetter (CoRR, 2025)