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Found 4 papers, 3 papers with code
Predicting Open-Hole Laminates Failure Using Support Vector Machines With Classical and Quantum Kernels
Here, we also propose using data-based models but to tackle open hole composite failure from a classification point of view.
Using AI libraries for Incompressible Computational Fluid Dynamics
Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors).
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
The idea comes from the observation that convolutional layers can be used to express a discretisation as a neural network whose weights are determined by the numerical method, rather than by training, and hence, we refer to this approach as Neural Networks for PDEs (NN4PDEs).
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization.
