Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics

• URL: http://arxiv.org/abs/2511.04534v1
• Date: Thu, 06 Nov 2025 16:47:52 GMT
• Title: Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics
• Authors: Jonas E. Katona, Emily K. de Jong, Nipun Gunawardena,
• Abstract summary: Reduced-order models (ROMs) can efficiently simulate high-dimensional physical systems, but lack robust uncertainty quantification methods.<n>We introduce a model-agnostic framework for predictive uncertainty quantification in latent space ROMs.<n>We demonstrate the method on a latent space dynamical model for cloud microphysics, where it accurately predicts the evolution of droplet-size distributions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting [63.8116386935854]
  We demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized trainings.<n>We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector.<n>We find that our framework design is robust to the choice of probabilistic estimators, seamlessly supporting interpolants, diffusion models, and CRPS-based ensemble training.
  arXiv  Detail & Related papers  (2026-01-26T03:52:16Z)
• Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction [0.0]
  Parametric models with fixed nominal parameters often omit critical physical effects due to simplifications in geometry, material behavior, damping, or boundary conditions.<n>This work introduces a comprehensive framework for MFE estimation and correction in high-dimensional finite element based structural dynamical systems.<n>To ensure computational tractability, the FE system is projected onto a reduced modal basis, and a mesh-invariant neural network maps modal states to discrepancy estimates.
  arXiv  Detail & Related papers  (2026-01-08T10:14:27Z)
• Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows [1.8899300124593648]
  We introduce MCNF, a novel uncertainty quantification method that produces both prediction intervals and the full conditioned predictive distribution.<n>MCNF operates on top of the underlying trained predictive model; thus, no predictive model retraining is needed.<n>We provide experimental evidence that the MCNF-based uncertainty estimate is well calibrated, is competitive with state-of-the-art uncertainty quantification methods, and provides richer information for downstream decision-making tasks.
  arXiv  Detail & Related papers  (2025-11-30T11:08:40Z)
• Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts [100.26854618129039]
  Weather forecasting is fundamentally challenged by the chaotic nature of the atmosphere.<n>Recent advances in Bayesian Deep Learning (BDL) offer a promising but often disconnected alternative.<n>We bridge these paradigms through a unified hybrid BDL framework for ensemble weather forecasting.
  arXiv  Detail & Related papers  (2025-11-18T07:49:52Z)
• BLIPs: Bayesian Learned Interatomic Potentials [47.73617239750485]
  Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry.<n>MLIPs do not provide uncertainty estimates by construction, which are fundamental to guide active learning pipelines.<n>BLIP is a scalable, architecture-agnostic variational Bayesian framework for training or fine-tuning MLIPs.
  arXiv  Detail & Related papers  (2025-08-19T17:28:14Z)
• Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models [0.0]
  We propose a latent score-based generative AI framework for learning, non-local closure models and laws in nonlinear dynamical systems.<n>This work addresses a key challenge of modeling complex multiscale dynamical systems without a clear scale separation.
  arXiv  Detail & Related papers  (2025-06-25T19:04:02Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models [11.308331231957588]
  This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-23T10:01:39Z)
• Conformalized Multimodal Uncertainty Regression and Reasoning [0.9205582989348333]
  This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds. We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries can result in multimodal uncertainties.
  arXiv  Detail & Related papers  (2023-09-20T02:40:59Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Last layer state space model for representation learning and uncertainty quantification [0.0]
  We propose to decompose a classification or regression task in two steps: a representation learning stage to learn low-dimensional states, and a state space model for uncertainty estimation. We demonstrate how predictive distributions can be estimated on top of an existing and trained neural network, by adding a state space-based last layer. Our model accounts for the noisy data structure, due to unknown or unavailable variables, and is able to provide confidence intervals on predictions.
  arXiv  Detail & Related papers  (2023-07-04T08:37:37Z)
• Lightweight, Uncertainty-Aware Conformalized Visual Odometry [2.429910016019183]
  Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics. Emerging edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties. This paper presents a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands.
  arXiv  Detail & Related papers  (2023-03-03T20:37:55Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.