Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting

• URL: http://arxiv.org/abs/2606.21021v1
• Date: Fri, 19 Jun 2026 01:16:45 GMT
• Title: Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting
• Abstract summary: Long-horizon spacecraft trajectory forecasting suffers from error accumulation due to the absence of corrective observations in the forecast regime.<n>We introduce a Predictor--Corrector framework in which a physics-based continuous-time $textitdeterministic$ forecaster is augmented with a learned continuous-time $textitprobabilistic$ Corrector.<n>We evaluate the proposed framework on long-horizon spacecraft trajectory forecasting using real-world data from NASA.
• Score: 7.565108739173396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-horizon spacecraft trajectory forecasting suffers from error accumulation due to the absence of corrective observations in the forecast regime, making reliable uncertainty estimation crucial for safety-critical decision-making such as space domain awareness and conjunction assessment. While high-fidelity physics-based orbit propagators provide accurate deterministic forecasts, they typically lack calibrated uncertainty estimates over long horizons. We introduce a Predictor--Corrector framework in which a physics-based continuous-time $\textit{deterministic}$ forecaster is augmented with a learned continuous-time $\textit{probabilistic}$ Corrector that models forecast errors. The proposed Corrector can be wrapped around an existing deterministic propagator to improve forecast accuracy while producing sharp and calibrated full-covariance uncertainty estimates. The Corrector is based on Latent Neural Controlled Differential Equations (Latent NCDEs) and models the probabilistic temporal evolution of forecast errors in continuous time, naturally supporting irregular sampling and missing features. We further introduce a loss function that promotes calibration and sharpness in long-horizon uncertainty propagation. We evaluate the proposed framework on long-horizon spacecraft trajectory forecasting using real-world data from NASA's Crustal Dynamics Data Information System (CDDIS), wrapping the Corrector around NASA's General Mission Analysis Tool (GMAT). Across forecast horizons of 2--4 days without observations and six rolling test windows, the proposed approach consistently improves accuracy and uncertainty calibration compared to deterministic baselines and Latent ODE-based correctors, demonstrating the effectiveness of the continuous-time probabilistic Corrector for trajectory forecasting.

Related papers

• TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting [7.191212864694664]
  TSCoNet is a two-stage convolutional-recurrent model coupled with a Gaussian copula that jointly forecasts multiple variables over space and time.<n>We study the approach on simulated non-stationary spatial fields on the sphere and on a real dataset of monthly precipitation and temperature for fifty cities over 2000-2020.
  arXiv  Detail & Related papers  (2026-07-11T17:28:53Z)
• Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs [0.5076419064097734]
  Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.<n>We propose a probabilistic causal framework for generating temporal graphs with transient edges and known ground-truth causal structure.<n>For the proposed binary logistic parametrisation, we derive the Cramér--Rao bound and validate the tradeoff between parameter estimation error and irreducible predictive loss.<n>Our results show that predictive accuracy alone may not reflect whether a model has learned the underlying causal mechanism, motivating benchmarks that distinguish reducible model error from intrinsic process uncertainty.
  arXiv  Detail & Related papers  (2026-06-26T16:13:48Z)
• Rethinking Gaussian Trajectory Predictors: Calibrated Uncertainty for Safe Planning [4.546517638153619]
  Existing trajectory predictors primarily rely on the Negative Log-Likelihood loss, which is prone to predict over- or under-confident distributions.<n>This paper introduces a novel loss function for calibrating prediction uncertainty.<n>We show that our method significantly improves the reliability of confidence levels predicted by different State-Of-The-Art Gaussian trajectory predictors.
  arXiv  Detail & Related papers  (2026-03-11T04:42:49Z)
• Physically consistent and uncertainty-aware learning of spatiotemporal dynamics [44.27000400517127]
  We introduce a physics-consistent neural operator (PCNO) that enforces physical constraints.<n>A physics-consistent projection layer within PCNO efficiently computes mass momentum conservation in Fourier space.<n>We also propose DiffPCNO, which leverages a diffusion consistency model to quantify and quantify uncertainties.
  arXiv  Detail & Related papers  (2025-10-23T22:17:21Z)
• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors.<n>We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression [7.6146285961466]
  In this paper, different uncertainty estimation methods are compared to forecast meteorological time series data. Results show how each uncertainty estimation method performs on the forecasting task.
  arXiv  Detail & Related papers  (2022-11-11T14:29:13Z)
• Probabilistic Symmetry for Multi-Agent Dynamics [18.94585103009698]
  We propose a novel deep dynamics model, Probabilistic Equivariant Continuous COnvolution (PECCO) for probabilistic prediction of multi-agent trajectories. PECCO shows significant improvements in accuracy and calibration compared to non-equivariant baselines.
  arXiv  Detail & Related papers  (2022-05-04T07:51:08Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification [0.13999481573773068]
  Ensemble forecasting is, so far, the most successful approach to produce relevant forecasts along with an estimation of their uncertainty. Main limitations of ensemble forecasting are the high computational cost and the difficulty to capture and quantify different sources of uncertainty. In this work proof-of-concept model experiments are conducted to examine the performance of ANNs trained to predict a corrected state of the system and the state uncertainty using only a single deterministic forecast as input.
  arXiv  Detail & Related papers  (2021-11-29T16:52:17Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• 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)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

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.