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,
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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.
citing papers explorer
-
Statistical finite elements for sequential data synthesis in solid dynamics
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,
-
Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials
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.
-
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.