Duncan Dennis, Räth Christoph
Department of Physics, Ludwig-Maximilians-Universität, Schellingstraße 4, 80799 Munich, Germany.
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für KI Sicherheit, Wilhelm-Runge-Straße 10, 89081 Ulm, Germany.
Chaos. 2023 Oct 1;33(10). doi: 10.1063/5.0164013.
Hybrid reservoir computing combines purely data-driven machine learning predictions with a physical model to improve the forecasting of complex systems. In this study, we investigate in detail the predictive capabilities of three different architectures for hybrid reservoir computing: the input hybrid (IH), output hybrid (OH), and full hybrid (FH), which combines IH and OH. By using nine different three-dimensional chaotic model systems and the high-dimensional spatiotemporal chaotic Kuramoto-Sivashinsky system, we demonstrate that all hybrid reservoir computing approaches significantly improve the prediction results, provided that the model is sufficiently accurate. For accurate models, we find that the OH and FH results are equivalent and significantly outperform the IH results, especially for smaller reservoir sizes. For totally inaccurate models, the predictive capabilities of IH and FH may decrease drastically, while the OH architecture remains as accurate as the purely data-driven results. Furthermore, OH allows for the separation of the reservoir and the model contributions to the output predictions. This enables an interpretation of the roles played by the data-driven and model-based elements in output hybrid reservoir computing, resulting in higher explainability of the prediction results. Overall, our findings suggest that the OH approach is the most favorable architecture for hybrid reservoir computing, when taking accuracy, interpretability, robustness to model error, and simplicity into account.
混合储层计算将纯数据驱动的机器学习预测与物理模型相结合，以改进对复杂系统的预测。在本研究中，我们详细研究了三种不同的混合储层计算架构的预测能力：输入混合（IH）、输出混合（OH）和结合了IH与OH的全混合（FH）。通过使用九个不同的三维混沌模型系统以及高维时空混沌的Kuramoto-Sivashinsky系统，我们证明，只要模型足够准确，所有混合储层计算方法都会显著改善预测结果。对于准确的模型，我们发现OH和FH的结果相当，并且显著优于IH的结果，尤其是对于较小的储层规模。对于完全不准确的模型，IH和FH的预测能力可能会急剧下降，而OH架构仍与纯数据驱动的结果一样准确。此外，OH允许区分储层和模型对输出预测的贡献。这使得能够解释数据驱动和基于模型的元素在输出混合储层计算中所起的作用，从而提高预测结果的可解释性。总体而言，我们的研究结果表明，在考虑准确性、可解释性、对模型误差的鲁棒性和简单性时，OH方法是混合储层计算中最有利的架构。
