A Bayesian filtering extension of statFEM assimilates sequential observational data into elastodynamic finite element models by modeling uncertainties as Gaussian random fields, advancing the state with a stochastic Newmark scheme, and approximating the non-Gaussian prior via perturbation to obtain,
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A Bayesian active learning method with statistical feature engineering and multi-output Gaussian processes selects target hyperelastic metamaterial designs from 50,000 candidates using under 0.5% high-fidelity oracle calls.
For random 2-layer ReLU networks the dominant eigenspaces of the Fisher information matrix are spanned by spherical harmonics of degree ≤2 and capture 97.7% of the trace independently of parameter count.
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Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information
For random 2-layer ReLU networks the dominant eigenspaces of the Fisher information matrix are spanned by spherical harmonics of degree ≤2 and capture 97.7% of the trace independently of parameter count.