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Found 5 papers, 1 papers with code
Solving multiscale elliptic problems by sparse radial basis function neural networks
Thus the loss function contains two parts: the $L_2$ loss for the residual of the first-order system and boundary conditions, and the $\ell_1$ regularization term for the weights of radial basis functions (RBFs).
A Gauss-Seidel projection method with the minimal number of updates for stray field in micromagnetic simulations
However, GSPM-BDF2 updates the stray field only once per time step, leading to an efficiency improvement of about $60\%$ than the state-of-the-art GSPM for micromagnetic simulations.
Numerical Analysis Numerical Analysis
Enforcing exact boundary and initial conditions in the deep mixed residual method
In theory, boundary and initial conditions are important for the wellposedness of partial differential equations (PDEs).
Numerical Analysis Numerical Analysis
Quasi-Monte Carlo sampling for machine-learning partial differential equations
Often, a deep neural network outperforms classical numerical methods in terms of both accuracy and efficiency.
A QMC-deep learning method for diffusivity estimation in random domains
Exciton diffusion plays a vital role in the function of many organic semiconducting opto-electronic devices, where an accurate description requires precise control of heterojunctions.
