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

TL;DR

This paper presents CANUF, a neurosymbolic framework that integrates Bayesian deep learning with symbolic constraints to provide trustworthy, physically consistent uncertainty estimates for scientific AI applications.

Contribution

It introduces the first end-to-end differentiable pipeline combining uncertainty quantification, symbolic constraint satisfaction, and interpretability for scientific modeling.

Findings

Reduces Expected Calibration Error by 34.7% compared to Bayesian neural networks.

Maintains 99.2% constraint satisfaction across benchmarks.

Constraint-guided recalibration improves performance by 18.3%.

Abstract

Scientific Artificial Intelligence (AI) applications require models that deliver trustworthy uncertainty estimates while respecting domain constraints. Existing uncertainty quantification methods lack mechanisms to incorporate symbolic scientific knowledge, while neurosymbolic approaches operate deterministically without principled uncertainty modeling. We introduce the Constraint-Aware Neurosymbolic Uncertainty Framework (CANUF), unifying Bayesian deep learning with differentiable symbolic reasoning. The architecture comprises three components: automated constraint extraction from scientific literature, probabilistic neural backbone with variational inference, and differentiable constraint satisfaction layer ensuring physical consistency. Experiments on Materials Project (140,000+ materials), QM9 molecular properties, and climate benchmarks show CANUF reduces Expected Calibration Error by 34.7% versus Bayesian neural networks while maintaining 99.2% constraint satisfaction. Ablations reveal constraint-guided recalibration contributes 18.3% performance gain, with constraint extraction achieving 91.4% precision. CANUF provides the first end-to-end differentiable pipeline simultaneously addressing uncertainty quantification, constraint satisfaction, and interpretable explanations for scientific predictions.