We present a simple and scalable implementation of next-generation reservoir computing (NGRC) for modeling dynamical systems from time-series data. The method uses a pseudorandom nonlinear projection of time-delay embedded inputs, allowing the feature-space dimension to be chosen independently of the observation size and offering a flexible alternative to polynomial-based NGRC projections. We demonstrate the approach on benchmark tasks, including attractor reconstruction and bifurcation diagram estimation, using partial and noisy measurements. We further show that small amounts of measurement noise during training act as an effective regularizer, improving long-term autonomous stability compared to standard regression alone. Across all tests, the models remain stable over long rollouts and generalize beyond the training data. The framework offers explicit control of system state during prediction, and these properties make NGRC a natural candidate for applications such as surrogate modeling and digital-twin applications.

翻译：我们提出了一种简单且可扩展的下一代储层计算实现方法，用于基于时间序列数据对动力学系统进行建模。该方法采用时滞嵌入输入的伪随机非线性投影，使得特征空间维度可以独立于观测规模进行选择，并为基于多项式的NGRC投影提供了一种灵活的替代方案。我们通过部分观测与含噪声测量下的基准任务（包括吸引子重构与分岔图估计）验证了该方法的有效性。我们进一步证明，训练过程中引入少量测量噪声可作为一种有效的正则化手段，相较于单纯的标准回归方法，能显著提升长期自主稳定性。在所有测试中，模型在长期推演中保持稳定，并能泛化至训练数据之外。该框架为预测过程中的系统状态提供了显式控制，这些特性使得NGRC成为代理建模与数字孪生等应用领域的理想选择。