4 citations · 14 across the 9 of their papers we have counts for
10 papers
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Thorben Prein, Elton Pan, Sami Haddouti +8
Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel…
LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
Florian Sestak, Artur Toshev, Andreas Fürst +3
Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space m…
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Artur P. Toshev, Harish Ramachandran, Jonas A. Erbesdobler +3
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The…
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams +1
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finit…
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Artur P. Toshev, Gianluca Galletti, Fabian Fritz +2
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizati…
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev +1
Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property p…
Learning Lagrangian Fluid Mechanics with E()-Equivariant Graph Neural Networks
Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter +2
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accur…
E() Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter +2
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accur…
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Artur P. Toshev, Ludger Paehler, Andrea Panizza +1
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In t…