Výsledky vyhledávání - "Andreas Lintermann"

Akademický článek

Parallel and scalable AI in HPC systems for CFD applications and beyond

Autor: Rakesh Sarma, Eray Inanc, Marcel Aach, Andreas Lintermann

Publikováno v: Frontiers in High Performance Computing, Vol 2 (2024)

This manuscript presents the library AI4HPC with its architecture and components. The library enables large-scale trainings of AI models on High-Performance Computing systems. It addresses challenges in handling non-uniform datasets through data mani

Externí odkaz: https://doaj.org/article/1de2ecdf8f244483a492e7adc9d72a3a

Zobrazit plný text záznamu

Akademický článek

Prediction of Turbulent Boundary Layer Flow Dynamics with Transformers

Autor: Rakesh Sarma, Fabian Hübenthal, Eray Inanc, Andreas Lintermann

Publikováno v: Mathematics, Vol 12, Iss 19, p 2998 (2024)

Time-marching of turbulent flow fields is computationally expensive using traditional Computational Fluid Dynamics (CFD) solvers. Machine Learning (ML) techniques can be used as an acceleration strategy to offload a few time-marching steps of a CFD s

Externí odkaz: https://doaj.org/article/e2c8ecbb13f54762b3319faba6633606

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks

Autor: Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann

Publikováno v: Journal of Big Data, Vol 10, Iss 1, Pp 1-23 (2023)

Abstract Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an analysis within a realistic timeframe. Deep learning models have proven to be capable of underst

Externí odkaz: https://doaj.org/article/1ea7daa2801f435cb665bebd532759dc

Zobrazit plný text záznamu

Akademický článek

Turbulent Flow Prediction-Simulation: Strained Flow with Initial Isotropic Condition Using a GRU Model Trained by an Experimental Lagrangian Framework, with Emphasis on Hyperparameter Optimization

Autor: Reza Hassanian, Marcel Aach, Andreas Lintermann, Ásdís Helgadóttir, Morris Riedel

Publikováno v: Fluids, Vol 9, Iss 4, p 84 (2024)

This study presents a novel approach to using a gated recurrent unit (GRU) model, a deep neural network, to predict turbulent flows in a Lagrangian framework. The emerging velocity field is predicted based on experimental data from a strained turbule

Externí odkaz: https://doaj.org/article/6eadbcce9cbd4b0a853fb639370a94b0

Zobrazit plný text záznamu

Akademický článek

Hybrid datasets: Incorporating experimental data into Lattice‐Boltzmann simulations

Autor: Helen E. Morrison, Andreas Lintermann, Sven Grundmann

Publikováno v: Engineering Reports, Vol 2, Iss 6, Pp n/a-n/a (2020)

Summary A novel method, which combines both fluid‐mechanical experimental and numerical data from magnetic resonance velocimetry and Lattice‐Boltzmann (LB) simulations is presented. The LB method offers a unique and simple way of integrating the

Externí odkaz: https://doaj.org/article/93992b7011004f5eb2447d53adc54f5b

Zobrazit plný text záznamu

Effects of the Nasal Cavity Complexity on the Pharyngeal Airway Fluid Mechanics: A Computational Study

Autor: Hussein Aljawad, Wolfgang Schroöder, Andreas Lintermann, Mario Rüttgers, Kyung Min Lee

Publikováno v: Journal of digital imaging 34, 1120–1133 (2021). doi:10.1007/s10278-021-00501-x
J Digit Imaging

The impact of the human nasal airway complexity on the pharyngeal airway fluid mechanics is investigated at inspiration. It is the aim to find a suitable degree of geometrical reduction that allows for an efficient segmentation of the human airways f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2095097531f50ea9bf6b7c95b041bea0
https://doi.org/10.1007/s10278-021-00501-x

Zobrazit plný text záznamu

Lattice–Boltzmann simulations for complex geometries on high-performance computers

Autor: Andreas Lintermann, Wolfgang Schröder

Publikováno v: CEAS Aeronautical Journal 11, 745-766 (2020). doi:10.1007/s13272-020-00450-1

Complex geometries pose multiple challenges to the field of computational fluid dynamics. Grid generation for intricate objects is often difficult and requires accurate and scalable geometrical methods to generate meshes for large-scale computations.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72f9a4817a0c67830bbb341f7f374418
https://doi.org/10.1007/s13272-020-00450-1

Zobrazit plný text záznamu

Virtual Surgeries of Nasal Cavities Using a Coupled Lattice-Boltzmann–Level-Set Approach

Autor: Moritz Waldmann, Mario Rüttgers, Andreas Lintermann, Wolfgang Schröder

Publikováno v: Journal of Engineering and Science in Medical Diagnostics and Therapy. 5

Fluid mechanical properties of respiratory flow such as pressure loss, temperature distribution, or wall-shear stress characterize the physics of a nasal cavity. Simulations based on computational fluid dynamics (CFD) methods are able to deliver in-d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::41e6f881c044782f1e23bce82c0b2acf
https://doi.org/10.1115/1.4054042

Zobrazit plný text záznamu

Correction to: Machine-Learning-Based Control of Perturbed and Heated Channel Flows

Autor: Wolfgang Schröder, Andreas Lintermann, Mario Rüttgers, Moritz Waldmann

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030905385

Chapter “Machine-Learning-Based Control of Perturbed and Heated Channel Flows” was previously published non-open access. It has now been changed to open access under a CC BY 4.0 license and the copyright holder updated to ‘The Author(s)’.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::512adf769c69d04003c00332bf86f32a
https://doi.org/10.1007/978-3-030-90539-2_37

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání