2 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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…
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…