arXiv:2511.08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences. Within physical sciences the main focus has been on discovery of dynamical systems from observational data. Yet the reliability of learned surrogates and symbolic models is often undermined by the fundamental problem of non-uniqueness. The resulting models may fit the available data perfectly, but lack genuine predictive power. This raises the question: under what conditions can the systems governing equations be uniquely identified from a finite set of
The proliferation of AI in scientific discovery, particularly in deep learning models for dynamical systems, necessitates a deeper understanding of their reliability and limits.
This research addresses the fundamental challenge of interpretability and trustworthiness in AI-driven scientific discovery, impacting the genuine predictive power of learned models.
The criteria for 'discoverability' in AI-assisted scientific modeling become clearer, emphasizing the inherent properties of systems like chaos for reliable identification.
- · Theoretical AI researchers
- · Physical scientists
- · Domain experts integrating AI
- · Robust AI model developers
- · Developers of unreliable AI surrogates
- · Fields over-reliant on 'black box' AI discovery
- · AI models lacking strong theoretical grounding
Increased scrutiny on the uniqueness and explanatory power of AI-derived scientific models.
Development of new AI architectures and methodologies specifically designed for 'discoverable' systems.
A potential re-evaluation of 'AI in science' hype, grounding expectations in the fundamental limits of data-driven discovery.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI