Scientifically-Interpretable Reasoning Network (ScIReN): Uncovering the Black-Box of Nature

2025-06-16

AI Summary

Existing neural networks lack physical interpretability, while mechanistic models rely heavily on empirical parameters and exhibit poor cross-scale generalizability; current hybrid approaches struggle to invert scientifically meaningful latent parameters from data. Method: We propose an end-to-end differentiable neuro-mechanistic fusion framework: (i) an interpretable encoder learns latent parameters with biogeochemical significance; (ii) a differentiable process model decodes predictions; and (iii) a hard-Sigmoid constraint layer embeds scientific priors to ensure traceable mapping from latent parameters to underlying mechanisms. Results: Applied to soil organic carbon flux and ecosystem respiration modeling, our method surpasses purely data-driven models in predictive accuracy and—uniquely—quantifies causal relationships between input features and mechanistic processes. It thus unifies high prediction fidelity with mechanistic interpretability, enabling physically grounded, data-informed inference.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Neural networks are a powerful tool for learning patterns from data. However, they do not respect known scientific laws, nor can they reveal novel scientific insights due to their black-box nature. In contrast, scientific reasoning distills biological or physical principles from observations and controlled experiments, and quantitatively interprets them with process-based models made of mathematical equations. Yet, process-based models rely on numerous free parameters that must be set in an ad-hoc manner, and thus often fit observations poorly in cross-scale predictions. While prior work has embedded process-based models in conventional neural networks, discovering interpretable relationships between parameters in process-based models and input features is still a grand challenge for scientific discovery. We thus propose Scientifically-Interpretable Reasoning Network (ScIReN), a fully-transparent framework that combines interpretable neural and process-based reasoning. An interpretable encoder predicts scientifically-meaningful latent parameters, which are then passed through a differentiable process-based decoder to predict labeled output variables. ScIReN also uses a novel hard-sigmoid constraint layer to restrict latent parameters to meaningful ranges defined by scientific prior knowledge, further enhancing its interpretability. While the embedded process-based model enforces established scientific knowledge, the encoder reveals new scientific mechanisms and relationships hidden in conventional black-box models. We apply ScIReN on two tasks: simulating the flow of organic carbon through soils, and modeling ecosystem respiration from plants. In both tasks, ScIReN outperforms black-box networks in predictive accuracy while providing substantial scientific interpretability -- it can infer latent scientific mechanisms and their relationships with input features.

Problem

• Develop transparent model combining neural and process-based reasoning
• Discover interpretable relationships in scientific process-based models
• Enhance predictive accuracy while maintaining scientific interpretability

Innovation

• Combines interpretable neural and process-based reasoning
• Uses hard-sigmoid constraint for scientifically meaningful parameters
• Differentiable process-based decoder enhances interpretability and accuracy