# Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/constraint-aware-neurosymbolic-uncertainty-quantification-with-bayesian-deep-2/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shahnawaz Alam,Mohammed Mudassir Uddin,M. A. Aleem Pasha |
| Citations | 0 |
| Fields | Computer Science,Materials Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2601.12442 |
| OpenAlex ID | https://openalex.org/W7125350993 |
| Type | preprint |
| Year | 2026 |

## Paper authors

- [M. A. Aleem Pasha](https://scholariq.org/researchers/m-a-aleem-pasha/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Machine Learning in Materials Science](https://scholariq.org/topics/machine-learning-in-materials-science/)

## Paper topics

- [Machine Learning in Materials Science](https://scholariq.org/topics/machine-learning-in-materials-science/)
- [Explainable Artificial Intelligence (XAI)](https://scholariq.org/topics/explainable-artificial-intelligence-xai/)
- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.