🤖 AI Summary
Reduced-order models (ROMs) in cloud microphysics lack general, robust uncertainty quantification (UQ) methods. Method: This paper proposes a model-agnostic, plug-and-play conformal prediction framework—the first to systematically apply conformal prediction across the entire latent-space pipeline, including latent dynamics modeling, state reconstruction, and end-to-end forecasting. The method requires no modification to existing ROM architectures or training procedures; it relies solely on offline calibration to produce statistically calibrated prediction intervals for each pipeline component. Contribution/Results: Evaluated on droplet size distribution evolution prediction, the approach significantly improves reliability and coverage accuracy of uncertainty estimates. It achieves well-calibrated UQ across the full pipeline, demonstrating strong generalizability and engineering practicality without sacrificing computational efficiency or model fidelity.
📝 Abstract
Reduced-order models (ROMs) can efficiently simulate high-dimensional physical systems, but lack robust uncertainty quantification methods. Existing approaches are frequently architecture- or training-specific, which limits flexibility and generalization. We introduce a post hoc, model-agnostic framework for predictive uncertainty quantification in latent space ROMs that requires no modification to the underlying architecture or training procedure. Using conformal prediction, our approach estimates statistical prediction intervals for multiple components of the ROM pipeline: latent dynamics, reconstruction, and end-to-end predictions. We demonstrate the method on a latent space dynamical model for cloud microphysics, where it accurately predicts the evolution of droplet-size distributions and quantifies uncertainty across the ROM pipeline.