Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Session Overview |
Session | |||||||||
A26_01: Artificial Intelligence in Turbulence
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Presentations | |||||||||
3:30pm - 3:45pm
Parameter sensitivity analysis of a direct numerical simulation with heat release model as an analogy to bushfires. (YSA) Monash University
3:45pm - 4:00pm
Combining deep neural networks and a differentiable lattice Boltzmann solver for wall model prediction in large eddy simulations 1Autodesk Research; 2NVIDIA Corp
4:00pm - 4:15pm
A machine-learning-based zonal approach for turbulence modeling Politecnico di Milano
4:15pm - 4:30pm
Convolution-compacted vision transformers for wall heat-flux modelling in turbulent channel flow FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
4:30pm - 4:45pm
Data-driven based scale-adaptive turbulence closure modeling von Karman Institute for fluid dynamics
4:45pm - 5:00pm
Easy-attention-based transformer for temporal predictions of turbulent flows (YSA) 1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden; 2nstituto Universitario de Matem´atica Pura y Aplicada, Universitat Polit`ecnica de Val`encia. Camino de Vera s/n, 46022 Val`encia, Spain; 3Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
5:00pm - 5:15pm
Embedded learning of a wall model for separated flows 1The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China; 2School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
5:15pm - 5:30pm
Machine learning and CFD can work together for surgery planning in the human nose Politecnico di Milano
5:30pm - 5:45pm
Mean flow data assimilation of turbulent stenotic flow fields using physics-informed neural networks on 4D-flow MRI 1Laboratory for Flow Instability and Dynamics, Technische Universität Berlin, 10623 Berlin, Germany; 2Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, 10587 Berlin, Germany
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