Deep Learning for Fluid Simulation
A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Mathematical and Computational Fluid Mechanics".
Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 8340
Special Issue Editor
Special Issue Information
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
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Keywords
- Deep learning for fluids
- Encoder-decoder networks
- Generative adversarial networks
- Graph neural networks
- Physics-constrained neural networks
- Data-driven modelling
- Reduced order models
