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Found 4 papers, 2 papers with code
Bayesian Learning to Discover Mathematical Operations in Governing Equations of Dynamic Systems
Discovering governing equations from data is critical for diverse scientific disciplines as they can provide insights into the underlying phenomenon of dynamic systems.
Sparse Bayesian Deep Learning for Dynamic System Identification
Furthermore, a practical calculation approach based on the Monte-Carlo integration method is derived to quantify the uncertainty of the parameters and predictions.
A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark
This raises the challenge that how to train a neural network for system identification with a small dataset.
BayesNAS: A Bayesian Approach for Neural Architecture Search
One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training.
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