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

• URL: http://arxiv.org/abs/2506.14054v1
• Date: Mon, 16 Jun 2025 23:21:37 GMT
• Title: Scientifically-Interpretable Reasoning Network (ScIReN): Uncovering the Black-Box of Nature
• Abstract summary: We propose a fully-transparent framework that combines interpretable neural and process-based reasoning.<n>An interpretable encoder predicts scientifically-meaningful latent parameters, which are then passed through a differentiable process-based decoder.<n>ScIReN outperforms black-box networks in predictive accuracy while providing substantial scientific interpretability.
• Score: 19.959751739424785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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