让神经网络学会智能采样，提升对复杂物理系统的预测能力
Adaptive Sensing of Continuous Physical Systems for Machine Learning
- 用可训练注意力模块自动决定在哪测、怎么测
- 在典型混沌系统上预测准确率显著提升
- 适合研究物理信息融合与智能传感的学者
物理动力系统可视为自然的信息处理器：其状态能保存、转换并传播输入信息。这一视角启发我们不仅从系统生成的数据中学习，更应优化测量方式以提取对任务最有用的信息。本文提出一种通用的自适应信息提取计算框架，其中可训练的注意力模块学习探测系统状态的位置及如何组合测量值以优化预测性能。作为具体实现，我们以由偏微分方程描述的时空场为底层动态系统，但该框架适用于任何可采样的系统状态。实验表明，自适应空间感知显著提升了在经典混沌基准任务上的预测准确率。本工作将注意力增强的储备池计算视为更广泛范式的一个特例：神经网络作为可训练的测量设备，用于从物理动力系统中提取信息。
原文摘要 · Abstract (English)
Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning not only from data generated by such systems, but also how to measure them in a way that extracts the most useful information for a given task. We propose a general computing framework for adaptive information extraction from dynamical systems, in which a trainable attention module learns both where to probe the system state and how to combine these measurements to optimize prediction performance. As a concrete instantiation, we implement this idea using a spatiotemporal field governed by a partial differential equation as the underlying dynamics, though the framework applies equally to any system whose state can be sampled. Our results show that adaptive spatial sensing significantly improves prediction accuracy on canonical chaotic benchmarks. This work provides a perspective on attention-enhanced reservoir computing as a special case of a broader paradigm: neural networks as trainable measurement devices for extracting information from physical dynamical systems.
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