Summary of Diffhybrid-uq: Uncertainty Quantification For Differentiable Hybrid Neural Modeling, by Deepak Akhare et al.
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
by Deepak Akhare, Tengfei Luo, Jian-Xun Wang
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The hybrid neural differentiable models have made significant progress in scientific machine learning by integrating numerical representations of known physics into deep neural networks, enhancing predictive capabilities and showing great potential for data-driven modeling of complex physical systems. However, there is a critical challenge in quantifying the inherent uncertainties stemming from multiple sources. To address this gap, we introduce the novel method DiffHybrid-UQ for effective uncertainty propagation and estimation in hybrid models. This approach combines deep ensemble Bayesian learning with nonlinear transformations to discern and quantify aleatoric and epistemic uncertainties. The unscented transformation enables the flow of uncertainties through non-linear functions, while an ensemble of SGD trajectories estimates epistemic uncertainties. Our method offers a practical approximation to the posterior distribution of network parameters and physical parameters, making it suitable for parallel computing environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to predict complex physical systems using deep neural networks. They combine known physics with machine learning to make more accurate predictions. The problem is that this combination can be uncertain because of different sources of error. To solve this problem, they created a method called DiffHybrid-UQ that helps calculate these uncertainties and makes the predictions more reliable. |
Keywords
* Artificial intelligence * Machine learning * Stemming
