As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach. Input data are encoded as waves applied to our shallow water reservoir, and the readout wave height is obtained at a fixed detection point. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm, with the objective of maximizing the system's ability to linearly separate observations in the training data by maximizing the readout matrix determinant. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters. We also applied our approach to a regression task and show that our approach improves out-of-sample accuracy. Results from this study will inform how we interface with the physical reservoir in future work, and we will use these methods to continue to optimize other aspects of the physical implementation of this system as a computational reservoir.

翻译：随着计算资源需求达到前所未有的水平，研究正扩展到利用复杂材料基底进行计算的领域。在本研究中，我们与一家初创公司正在开发的水动力学系统模型进行交互，将其作为计算储层，并采用物质演化方法优化其特性。输入数据被编码为应用于浅水储层的波，并在固定检测点获取读出波高。我们通过演化搜索算法优化了读出时间以及输入映射到波幅或频率的方式，其目标是通过最大化读出矩阵行列式来最大化系统对训练数据中观测结果进行线性分离的能力。将演化方法应用于该储层系统后，与人工选择参数的实现相比，在XNOR任务上的可分离性显著提升。我们还将该方法应用于回归任务，并表明该方法提高了样本外准确率。本研究结果将为未来如何与物理储层交互提供指导，并将继续运用这些方法优化该系统作为计算储层的物理实现的其他方面。