AD-RaNN learns an effective low-dimensional sampling distribution for hidden parameters in randomized neural networks by optimizing a vector p via PDE-driven or data-driven adaptation and a two-stage least-squares procedure, improving accuracy on benchmark PDE problems.
A practical PINN framework for multi-scale problems with multi-magnitude loss terms.Journal of Computational Physics, 510:113112
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Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
AD-RaNN learns an effective low-dimensional sampling distribution for hidden parameters in randomized neural networks by optimizing a vector p via PDE-driven or data-driven adaptation and a two-stage least-squares procedure, improving accuracy on benchmark PDE problems.