In order to meet the demand for flow field prediction, this paper proposes KAN coupling model (KADS) combining Kolmogorov-Arnold network (KAN) and dynamic upsample (DySample: Upsampling by Dynamic Sampling), and uses two-dimensional diamond-shaped airfoil data to carry out flow field data prediction applications. In this paper, the activation function of the original KAN B-Spline is changed, and the KAN structures such as FourierKAN, GRBFKAN, RBFKAN, ChebyKAN are constructed, and their performance after coupling with DySample is evaluated. By comparing with the traditional MLP, it is found that ChebyKAN with Chebyshev polynomial as the activation function can achieve high accuracy with less training time and times, and there will be no overfitting during the test. The results show that the KADS model proposed in this paper can be applied to the task of flow field prediction and analysis, and can provide new modeling methods and ideas for the deep learning fluid intelligence modeling task.
Quantum computing is a new computing model based on the principles of quantum mechanics. Because of its powerful parallelism far superior to classical computing, quantum computing is considered as a computational method that may have a subversive impact on the future, providing a new way to solve some complex problems. The algorithms and applications of quantum solvers in numerical computation-related problems of large-scale science and engineering are reviewed. In particular, systems of linear equations, eigenvalue problems, differential equations, Hamiltonian and graph computation, quantum machine learning, quantum solver platform, and practical numerical simulation have been introduced. Aiming at different numerical computing problems, the current mainstream quantum computing algorithms are introduced in detail, and the research progress of relevant algorithms at home and abroad in recent years is comprehensively summarized. Finally, the future development trend of quantum computing in numerical algebra solving is prospected.
A lane changing model for multi-lane traffic flow is proposed.It makes use of advantages of Support Vector Machine (SVM) in a binary classification problem with multi-dimensional features and combines with Conserved Higher-Order traffic flow model (CHO) in Lagrange coordinates.The original data is generated with a fully discrete car following model and preprocessed by Synthetic Minority Oversampling Technique (SMOTE) algorithm.The SVM is trained with two indexes evaluation.It shows that the lane changing model based on SVM and CHO simulates effectively real multi-lane driving behavior based on current driving environment on expressway.