A neural network based framework to model particle rebound and fracture. Wear, (508-509):204476, Elsevier BV, November 2022. [PUMA: exc2075 peerreviewed pn1] URL
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. In Ivan Lirkov, und Svetozar Margenov (Hrsg.), Large-Scale Scientific Computing, 410--418, Springer International Publishing, Cham, 2022. [PUMA: exc2075 myown peerreviewed pn1 pn6]
Development of turbulent inflow methods for the high order HPC framework FLEXI. In Wolfgang E. Nagel, Dietmar H. Kröner, und Michael M. Resch (Hrsg.), High Performance Computing in Science and Engineering '21, 289--304, Springer International Publishing, Cham, 2023. [PUMA: exc2075 myown peerreviewed pn1]
Deep reinforcement learning for computational fluid dynamics on HPC systems. Journal of Computational Science, (65):101884, Elsevier BV, November 2022. [PUMA: exc2075 myown peerreviewed pn1] URL
Relexi — A scalable open source reinforcement learning framework for high-performance computing. Software Impacts, (14):100422, Elsevier BV, Dezember 2022. [PUMA: exc2075 myown peerreviewed pn1] URL
Deep reinforcement learning for turbulence modeling in large eddy simulations. International Journal of Heat and Fluid Flow, (99):109094, Elsevier BV, Februar 2023. [PUMA: exc2075 myown peerreviewed pn1] URL
Increasing the flexibility of the high order discontinuous Galerkin framework FLEXI towards large scale industrial applications. In Wolfgang E. Nagel, Dietmar H. Kröner, und Michael M. Resch (Hrsg.), High Performance Computing in Science and Engineering '20, Springer International Publishing, Cham, 2021. [PUMA: exc2075 myown peerReviewed pn1]
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. 2021. [PUMA: exc2075 peerreviewed pn1 pn6 preprint] URL
A data-driven high order sub-cell artificial viscosity for the discontinuous Galerkin spectral element method. Journal of Computational Physics, (441):110475, Elsevier BV, September 2021. [PUMA: EXC2075 peerreviewed pn1] URL
A perspective on machine learning methods in turbulence modeling. GAMM-Mitteilungen, (44)1:e202100002, 2021. [PUMA: EXC2075 myown peerReviewed pn1] URL