FLASH-MAX embeds exact Maxwell solutions as neurons in a neural network to reconstruct homogeneous EM fields from sparse data with guaranteed zero PDE residual and proven universal approximation on arbitrary domains.
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A hybrid FEM and ELM framework for parameter-dependent PDEs derives existence, uniqueness, regularity, and error estimates for inverse problems in photoacoustic tomography.
The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
R-HessELM with inclined entropy features predicts CHF from ECG signals with 98.49% accuracy.
Replacing SVD in ELM with LU, Hessenberg, Schur, Gram-Schmidt or Householder decompositions speeds training on large EEG BCI data, with Hessenberg preferred for pace and Householder for accuracy.
citing papers explorer
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Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
FLASH-MAX embeds exact Maxwell solutions as neurons in a neural network to reconstruct homogeneous EM fields from sparse data with guaranteed zero PDE residual and proven universal approximation on arbitrary domains.
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Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
A hybrid FEM and ELM framework for parameter-dependent PDEs derives existence, uniqueness, regularity, and error estimates for inverse problems in photoacoustic tomography.
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Random Vector Functional Link Neural Network based Ensemble Deep Learning
The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
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Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
R-HessELM with inclined entropy features predicts CHF from ECG signals with 98.49% accuracy.
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On improving learning capability of ELM and an application to brain-computer interface
Replacing SVD in ELM with LU, Hessenberg, Schur, Gram-Schmidt or Householder decompositions speeds training on large EEG BCI data, with Hessenberg preferred for pace and Householder for accuracy.