Deep Learning for Fluid Simulation

Dear Colleagues,

Deep neural networks are increasingly used for data-driven acceleration of fluid dynamics simulations. These models can be understood as nonlinear approximation methods for reproducing high-dimensional input-output maps based on nested layers of linear filters and non-linear activation functions. The filter weights and other hyperparameters are learned from a large corpus of training data through a stochastic gradient descent algorithm. In the case of deep learning for fluid simulations, the models can be trained with either computational fluid dynamics simulation data or actual flow measurement data. Various approaches have been proposed for tackling fluid dynamics simulation by deep learning, such as encoder-decoder and generative adversarial networks, graph neural networks, and convolutional networks for both particle-based flow simulations as well as continuous velocity field approximations. Current challenges in this topic include incorporating physical constraints of fluids to neural networks, learning model structure from experimental fluid measurements, simulating complex particulate and multi-phase flows, and generalizing flow behavior from limited training data.

Dr. Toni Lassila
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Deep learning for fluids
• Encoder-decoder networks
• Generative adversarial networks
• Graph neural networks
• Physics-constrained neural networks
• Data-driven modelling
• Reduced order models