Adaptive Sensing of Continuous Physical Systems for Machine Learning
arXiv:2603.03650v1 Announce Type: new Abstract: 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 provid
arXiv:2603.03650v1 Announce Type: new Abstract: 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.
Executive Summary
This article proposes a novel framework for adaptive information extraction from dynamical systems, leveraging a trainable attention module to optimize prediction performance. The approach is demonstrated using a spatiotemporal field governed by a partial differential equation, showcasing significant improvements in prediction accuracy on chaotic benchmarks. This work contributes to the development of neural networks as trainable measurement devices for extracting information from physical dynamical systems.
Key Points
- ▸ Adaptive information extraction from dynamical systems
- ▸ Trainable attention module for optimizing prediction performance
- ▸ Application to spatiotemporal fields governed by partial differential equations
Merits
Improved Prediction Accuracy
The proposed framework demonstrates significant improvements in prediction accuracy on canonical chaotic benchmarks
Flexibility and Generalizability
The approach can be applied to any system whose state can be sampled, making it a versatile tool for various applications
Demerits
Computational Complexity
The use of a trainable attention module may increase computational complexity, potentially limiting its applicability to large-scale systems
Limited Interpretability
The black-box nature of neural networks may make it challenging to interpret the results and understand the underlying dynamics of the system
Expert Commentary
The proposed framework represents a significant step forward in the development of neural networks as trainable measurement devices for extracting information from physical dynamical systems. By leveraging a trainable attention module, the approach can adaptively focus on the most relevant aspects of the system, leading to improved prediction accuracy. However, further research is needed to address the potential limitations, such as computational complexity and limited interpretability. The work has important implications for various fields, including climate modeling, fluid dynamics, and materials science, and can inform policy decisions in areas such as environmental monitoring and resource management.
Recommendations
- ✓ Further investigation into the computational complexity and potential optimizations of the proposed framework
- ✓ Exploration of the applicability of the approach to other types of dynamical systems, such as those governed by ordinary differential equations or stochastic processes
