This workshop will stimulate this research by providing a venue to exchange new ideas and discuss challenges and opportunities as well as expose this newly emerging field to a broader research community. We aim to bring together researchers and industrial practitioners working on any aspects of applying ML to the CFD and related domains in order to provide a venue for discussion, knowledge transfer, and collaboration among the research community.
We are soliciting papers on all aspects of CFD where ML plays a significant role or enables the solution of complex problems in CFD and related fields. Topics of interest include, but are not limited to: physics-based modeling with the main focus on fluid physics, such as reduced modeling for dimensionality reduction and the Reynolds-averaged Navier-Stokes (RANS) turbulence modeling; shape and topology optimization in solids; prediction of aeroacoustics; uncertainty quantification and reliability analysis; reinforcement learning for the design of active/passive flow control, and any ML approach that enables or enhances any of the above techniques.
The workshop will consist of 20-minute talks and will conclude with a panel session, where experts working in the field will discuss the most pressing challenges and attempt to identify the most promising directions to continue developing in the near future. Accepted papers will be published in a Springer LNCS proceedings volume that will accompany the ISC proceedings volume.