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ISC CFDML 2020 : FIRST INTERNATIONAL WORKSHOP ON THE APPLICATION OF MACHINE LEARNING TECHNIQUES TO COMPUTATIONAL FLUID DYNAMICS SIMULATIONS AND ANALYSIS

Frankfurt, Germany
Event Date: Jun 25, 2020 - Jun 25, 2020
Submission Deadline: Mar 30, 2020
Notification of Acceptance: Apr 27, 2020
Camera Ready Version Due : Jun 01, 2020

About

The combination of computational fluid dynamics (CFD) with machine learning (ML) is a newly emerging research direction with the potential to enable solving so far unsolved problems in many application domains. Machine learning is already applied to a number of problems in CFD, such as identification and extraction of hidden features in large-scale flow computations, finding undetected correlations between dynamical features of the flow, and generating synthetic CFD datasets through high-fidelity simulations. These approaches are forming a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of such features, and enabling deeper insight into the physics involved in complex natural processes.

Apart from pure fluid dynamics, the community is embracing ML in a number of areas, such as constitutive modeling of heterogeneous materials, multiphase flow modeling, dynamics of the atmospheric, ocean, and climate system, and combustion/chemical reactions.

The 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.

Call For Paper

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.

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