Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.

翻译：过程基建模（PBM）与机器学习（ML）在地球科学中常被视为不同范式。本文提出可微地球科学建模作为消除两者间认知壁垒并引发范式变革的强大路径。数十年来，PBM在可解释性与物理一致性方面具有优势，但难以高效利用大规模数据集。ML方法（尤其是深度网络）展现出强劲的预测能力，却缺乏回答特定科学问题的能力。尽管已有多种方法实现ML-物理融合，但关键基础主题——可微建模——尚未得到充分认知。本文系统阐述了可微地球科学建模（DG）的概念、适用性与重要性。"可微"指准确高效计算模型变量梯度，这为学习高维未知关系提供了关键支撑。DG涵盖将不同先验知识与神经网络连接并协同训练的一系列方法，其范畴不同于物理引导的机器学习，且强调第一性原理。初步证据表明，相比ML，DG具有更优的可解释性与因果性，更强的泛化与外推能力，以及知识发现的巨大潜力，同时性能趋近纯数据驱动ML方法。DG模型所需训练数据更少，且随着数据量增加，其性能与效率呈现有利的扩展趋势。借助DG，地球科学家能更有效地构建和研究问题、检验假设、发现未知关联。