Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Adami, Stefan"'
Autor:
Fan, Yuzhe, Bußmann, Alexander, Reuter, Fabian, Bao, Hengzhu, Adami, Stefan, Gordillo, José M., Adams, Nikolaus, Ohl, Claus-Dieter
We reveal for the first time by experiments that within a narrow parameter regime, two cavitation bubbles with identical energy generated in anti-phase develop a supersonic jet. High-resolution numerical simulation shows a mechanism for jet amplifica
Externí odkaz:
http://arxiv.org/abs/2401.02320
We present a Weakly Compressible SPH (WCSPH) formulation with a temporally variable speed of sound. The benefits of a time-varying sound speed formulation and the weaknesses of a constant sound speed formulation are worked out. It is shown how a vari
Externí odkaz:
http://arxiv.org/abs/2310.04139
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
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
Autor:
Biller, Benedikt, Bußmann, Alexander, Messe, Olivier, Giglmaier, Marcus, Adami, Stefan, Adams, Nikolaus A.
Publikováno v:
In Surface & Coatings Technology 30 October 2024 494 Part 1
Publikováno v:
In Journal of Computational Physics 1 December 2024 518
Publikováno v:
In Computers and Fluids 30 June 2024 278
In this work, physics-informed neural networks are applied to incompressible two-phase flow problems. We investigate the forward problem, where the governing equations are solved from initial and boundary conditions, as well as the inverse problem, w
Externí odkaz:
http://arxiv.org/abs/2101.09833
Numerical investigation of compressible flows faces two main challenges. In order to accurately describe the flow characteristics, high-resolution nonlinear numerical schemes are needed to capture discontinuities and resolve wide convective, acoustic
Externí odkaz:
http://arxiv.org/abs/2012.04385