1 citations · 1 across the 1 of their papers we have counts for
5 papers
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…
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…
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…