Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond
Joshua Fan, Haodi Xu, Feng Tao, Md Nasim, Marc Grimson, Yiqi Luo, Carla P. Gomes

TL;DR
The paper introduces ScIReN, a transparent neural network framework that combines scientific knowledge with data-driven learning to uncover hidden relationships in the soil carbon cycle and ecosystem respiration.
Contribution
It develops a novel interpretable neural network architecture that integrates process-based models with learnable parameters, revealing scientific relationships while maintaining high predictive accuracy.
Findings
ScIReN outperforms black-box models in predictive tasks.
It reveals interpretable relationships between inputs and latent parameters.
The framework successfully models soil carbon flow and ecosystem respiration.
Abstract
Soils have potential to mitigate climate change by sequestering carbon from the atmosphere, but the soil carbon cycle remains poorly understood. Scientists have developed process-based models of the soil carbon cycle based on existing knowledge, but they contain numerous unknown parameters and often fit observations poorly. On the other hand, neural networks can learn patterns from data, but do not respect known scientific laws, and are too opaque to reveal novel scientific relationships. 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. While the process-based decoder enforces existing…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Scientific Computing and Data Management · Machine Learning in Materials Science
MethodsSparse Evolutionary Training
