Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
arXiv:2604.24662v2 Announce Type: replace-cross Abstract: Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge is that the state variables are not directly observable and must be inferred from raw high-dimensional data without supervision. Here we introduce DySIB (Dynamical Symmetric Information Bottleneck) as a method to learn low-dimensional representations of time-series data by maximizing predictive mutual information between past and future observation windows while penalizing representation c
The paper introduces a novel method (DySIB) to extract meaningful dynamical state variables from complex, high-dimensional experimental data, which is becoming increasingly common in scientific research.
This development could significantly accelerate scientific discovery and engineering applications by enabling more efficient and accurate modelling of dynamic systems, impacting fields from material science to climate modeling.
The ability to infer underlying state variables from raw data without supervision offers a more robust and automated approach to understanding complex systems, reducing reliance on manual feature engineering.
- · AI researchers (Machine Learning)
- · Physicists and Data Scientists
- · Experimental scientists
- · Automation sector
- · Traditional manual data analysis approaches
Improved understanding and predictive modeling of highly complex physical and biological systems.
Acceleration of research and development in areas reliant on interpreting time-series data, potentially leading to new material discoveries or medical breakthroughs.
Enhanced capabilities for AI agents to interpret and control dynamic physical environments in real-time.
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Read at arXiv cs.AI