arXiv:2605.31547v1 Announce Type: new Abstract: Dynamical systems reconstruction (DSR) aims to learn surrogate models that capture the dynamics underlying time-series data. Reliably deploying these surrogates requires uncertainty estimates consistent with the learned dynamics. We expose a dynamic-probabilistic consistency (DPC) gap: the pursuit of finite-horizon probabilistic objectives can degrade dynamics or decouple predictive uncertainty from the local tangent dynamics it ought to reflect. We isolate three mechanisms behind this gap: core collapse, noise masking, and blind uncertainty. Spe
The increasing sophistication and widespread deployment of AI models for real-world dynamic systems necessitate robust uncertainty quantification, which this research directly addresses.
Reliable AI surrogates are critical for safety-critical applications like autonomous systems and climate modeling, preventing unpredictable failures due to unquantified dynamic-probabilistic inconsistencies.
This research provides a foundational understanding of critical limitations in current AI models used for dynamic systems, guiding the development of more robust, trustworthy AI.
- · AI safety researchers
- · Developers of predictive AI models
- · Industries relying on complex simulations
- · Developers of naive surrogate models
- · Applications with unquantified chaotic dynamics
Improved methodologies for building and evaluating AI models that simulate complex, dynamic systems.
Accelerated development of AI in fields requiring high-fidelity simulation and predictive certainty, such as aerospace or climate science.
Enhanced trust in AI-driven predictions and autonomous systems, potentially enabling broader adoption in sensitive domains.
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Read at arXiv cs.LG