Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Toshev, Artur P."'
Autor:
Toshev, Artur P., Ramachandran, Harish, Erbesdobler, Jonas A., Galletti, Gianluca, Brandstetter, Johannes, Adams, Nikolaus A.
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 recent addition of machine learning methods to the toolbox for solving
Externí odkaz:
http://arxiv.org/abs/2403.04750
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field
Externí odkaz:
http://arxiv.org/abs/2402.06275
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces o
Externí odkaz:
http://arxiv.org/abs/2309.16342
Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property prediction and molecular simulations. Nonetheless, as the field has bee
Externí odkaz:
http://arxiv.org/abs/2306.14818
Autor:
Toshev, Artur P., Galletti, Gianluca, Brandstetter, Johannes, Adami, Stefan, Adams, Nikolaus A.
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 accurate dynamic-interaction models than their non-equivariant counterparts
Externí odkaz:
http://arxiv.org/abs/2305.15603
Autor:
Toshev, Artur P., Galletti, Gianluca, Brandstetter, Johannes, Adami, Stefan, Adams, Nikolaus A.
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 accurate dynamic-interaction models than their non-equivariant counterparts
Externí odkaz:
http://arxiv.org/abs/2304.00150
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 this survey, we begin by providing an example of this with the parallel
Externí odkaz:
http://arxiv.org/abs/2304.00146