In this work, a novel deep learning method, called Bilateral Convolutional Neural Network (BiCNN), is proposed and then employed to accurately model dynamic wind farm wakes based on flow field data generated by high-fidelity simulations. Different from the existing machine-learning-based dynamic ...
In this paper, four machine learning techniques are employed for the mean flow velocity prediction, separately for sand-bed and gravel-bed rivers, namely artificial neural networks, adaptive-network-based fuzzy inference system, symbolic regression based on genetic programming, and support vector ...
learning field. A previous study showed that calibration performance in imbalanced data is biased because ML-based models considered the majority class to be more important than the minority class23. Furthermore, we constructed machine learning models based on the best AUROC values. This metric was ...
Neural network-based pore flow field prediction in porous media using super resolution. Phys. Rev. Fluids 2022, 7, 074302. [Google Scholar] [CrossRef] Santos, J.E.; Xu, D.; Jo, H.; Landry, C.J.; Prodanović, M.; Pyrcz, M.J. PoreFlow-Net: A 3D convolutional neural network ...
Fast flow field prediction over airfoils using deep learning approach. Phys. Fluids 2019, 31, 057103. [Google Scholar] [CrossRef] Kochkov, D.; Smith, J.; Alieva, A.; Wang, Q.; Brenner, M.; Hoyer, S. Machine learning–accelerated computational fluid dynamics. Proc. Natl. Acad. Sci....
This form of learning can be employed with tools such as prediction, classification, and regression. In this type of learning it is very useful to relate the cost associated with labeling as being very high, which allows for a completely labeled training process, since simple examples may ...
materials and chemical reactions. We focused on those applications for which conventional machine learning methods have been developed and generally accepted as benchmarks in their field. In addition, we also compared our model with the top-performing ones on tasks from the Matbench25suite of bench...
Most of the work in early 2010s used traditional machine learning approaches to perform the detection task. With the emergence of deep learning algorithms, many research has been conducted to perform distraction detection using neural networks. Furthermore, most of the work in the field is ...
This question has no one-size-fits-all answer, as the best framework for machine learning will depend on your specific needs and goals. However, a few popular frameworks are widely used in the field of machine learning, such as TensorFlow, PyTorch, and Keras. ...
Different neural network model structures and machine learning classification and regression algorithms were evaluated and optimized to determine the best models that performs prediction accurately. The data for developing the models was collected from experiments in literature where two phase flow of air-...