Summary of Empowering Bayesian Neural Networks with Functional Priors Through Anchored Ensembling For Mechanics Surrogate Modeling Applications, by Javad Ghorbanian et al.
Empowering Bayesian Neural Networks with Functional Priors through Anchored Ensembling for Mechanics Surrogate Modeling Applications
by Javad Ghorbanian, Nicholas Casaprima, Audrey Olivier
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Bayesian neural network (BNN) training scheme based on anchored ensembling to integrate a priori knowledge available in the function space. The approach leverages low-rank correlations between NN parameters learned from pre-training to realizations of the functional prior, which is critical for transferring knowledge between the function-space and parameter-space priors. By casting NN training within a Bayesian framework, this method can quantify uncertainties, particularly epistemic uncertainties that arise from lack of training data. The authors demonstrate the algorithm’s performance on a 1D example and a multi-input-output materials surrogate modeling task, showcasing both accuracy and quality of uncertainty estimation for in-distribution and out-of-distribution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to improve computer simulations in fields like mechanics and materials science. They’re working with special kinds of neural networks that can learn from data, but also use prior knowledge to make predictions. This is helpful because it allows them to estimate how certain their results are. The team developed a new way to train these neural networks by using information about the relationships between different parts of the network. They tested this method on a simple problem and then a more complex one involving materials science, showing that it works well for both. |
Keywords
» Artificial intelligence » Neural network
