Summary of Probabilistic Neural Networks (pnns) For Modeling Aleatoric Uncertainty in Scientific Machine Learning, by Farhad Pourkamali-anaraki et al.
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
by Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 explores the application of probabilistic neural networks (PNNs) in modeling aleatoric uncertainty, a concept characterized by unequal variance or heteroscedasticity. Unlike traditional neural networks, PNNs generate probability distributions for target variables, enabling the estimation of both predicted means and intervals in regression scenarios. The authors develop a probabilistic distance metric to optimize PNN architecture and deploy PNNs in controlled datasets as well as a real-world material science case involving fiber-reinforced composites. The results demonstrate that PNNs effectively model aleatoric uncertainty, outperforming Gaussian process regression with remarkable accuracy (R-squared scores approaching 0.97) and correlation coefficients close to 0.80. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using special kinds of neural networks called probabilistic neural networks (PNNs) to understand how things can be unpredictable or uncertain. Neural networks are usually good at making predictions, but PNNs take it a step further by showing how likely something is to happen instead of just saying what will definitely happen. The researchers came up with a way to make the PNNs better and tested them on some controlled data sets as well as real-world materials like fiber-reinforced composites. They found that PNNs are really good at modeling this uncertainty, making them useful for scientific machine learning problems. |
Keywords
* Artificial intelligence * Machine learning * Probability * Regression
