Meskhidze, Helen (2024) Beyond Classification and Prediction: The Promise of Physics-Informed Machine Learning in Astronomy and Cosmology. [Preprint]
|
Text
Beyond Classification and Prediction Meskhidze.pdf Download (261kB) | Preview |
Abstract
Though the use of machine learning (ML) is ubiquitous in astrophysics and cosmology, many still see the opacity of ML algorithms as a major issue to their scientific utility. One way of addressing this opacity is through an emerging trend in ML research of "teaching" ML algorithms physical laws and domain-specific knowledge. "Physics-informed machine learning" (PIML), as this methodology is called, promises to produce better predictions and yield more interpretable algorithms. It does so by using physical principles in the training process and/or by using physical principles to guide the development of the neural network architecture. In this chapter, I investigate two uses of PIML in astronomy/cosmology, each a representative example of the two PIML methods. In both cases, PIML provides improvements in terms of the predictions and efficiency of ML algorithms. However, I argue that only in the second case does PIML offer any improvement in terms of the interpretability of the algorithms.
| Export/Citation: | EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL |
| Social Networking: |
| Item Type: | Preprint | ||||||
|---|---|---|---|---|---|---|---|
| Creators: |
|
||||||
| Additional Information: | Forthcoming in: Juan Durán and Giorgia Pozzi (Eds.) Philosophy of Science for Machine Learning: Core Issues and New Perspective, Synthese Library. | ||||||
| Keywords: | opacity, interpretability, understanding, machine learning, neural network, astronomy, cosmology | ||||||
| Subjects: | Specific Sciences > Physics > Astrophysics General Issues > Computer Simulation General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Physics |
||||||
| Depositing User: | Helen Meskhidze | ||||||
| Date Deposited: | 09 Feb 2024 22:02 | ||||||
| Last Modified: | 09 Feb 2024 22:02 | ||||||
| Item ID: | 23067 | ||||||
| Subjects: | Specific Sciences > Physics > Astrophysics General Issues > Computer Simulation General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Physics |
||||||
| Date: | February 2024 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/23067 |
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](http://philsci-archive.pitt.edu/style/images/feed-icon-32x32.png){kind=icon}