Reconstructing temperature fields in S-Duct based on pressure measurements using physics-informed neural networks

Jian Teng¹, Yiqing Li²

¹School of Naval Architecture and Ocean Engineering, Guangzhou Maritime University; ²School of Aircraft Engineering, Nanchang Hangkong University

Spectral adjoint-based assimilation of sparse data for augmented unsteady simulations of turbulent flows

Justin Plogmann^1,2, Oliver Brenner¹, Patrick Jenny¹

¹Institute of Fluid Dynamics, ETH Zurich, Sonneggstrasse3, CH-8092, Zürich, Switzerland; ²Automotive Powertrain Technologies Laboratory, Swiss Federal Laboratories for Materials Science and Technology (Empa), Überlandstrasse 129, CH-8600, Dübendorf, Switzerland

Subgrid-scale modeling of stratified turbulence using a constrained artificial neural network

Daisuke Nishiyama¹, Yuji Hattori²

¹Graduate School of Information Sciences, Tohoku University; ²Institute of Fluid Science, Tohoku University

Temporal forecasting of turbulent Rayleigh-Bénard convection using echo state networks

Mohammad Sharifi Ghazijahani, Christian Cierpka

Institute of Thermodynamics and Fluid Mechanics, Technische Universitat Ilmenau

Using the ZPG-TBL DNS data to benchmark the performance of Physics Informed Neural Network (PINN) for heat transfer modelling

Aakhash Sundaresan^1,2, Julio Soria², Atul Srivastava³, Callum Atkinson²

¹IITB Monash Research Academy, Indian Institute of Technology Bombay, Mumbai, Maharashtra - 400 076, India; ²Laboratory of Turbulence Research in Aerospace and Combustion, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC-3800, Melbourne, Australia; ³Department of Mechanical Engineering, Indian Institute of Technology, Bombay, India - 400 076

Learning the dynamics of symmetry-reduced chaotic attractors in fluid dynamics from data

Simon Kneer, Nazmi Burak Budanur

Max Planck Institute for the Physics of Complex Systems