用变分推断让数据融合模型自带不确定性，预测更准更可靠。
Uncertainty-aware data assimilation through variational inference
- 用多变量高斯分布建模预测状态，显式表达不确定性。
- 在洛伦兹-96系统上实现近乎完美的预测校准效果。
- 适合需要可靠置信度估计的气象、气候等动态系统建模者。
数据融合通过结合动力学模型与噪声且不完整的观测数据，以推断系统随时间的状态，通常涉及不确定性。我们基于现有的确定性机器学习方法，提出一种基于变分推断的扩展，使预测状态服从多元高斯分布。以混沌的洛伦兹-96动力系统为测试基准，结果表明，新模型可获得几乎完全校准的预测，并可集成到更广泛的变分数据融合流程中，从而在延长数据融合窗口时获得更大收益。代码已公开于 https://github.com/anthony-frion/Stochastic_CODA。
原文摘要 · Abstract (English)
Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing deterministic machine learning approach, we propose a variational inference-based extension in which the predicted state follows a multivariate Gaussian distribution. Using the chaotic Lorenz-96 dynamics as a testing ground, we show that our new model enables to obtain nearly perfectly calibrated predictions, and can be integrated in a wider variational data assimilation pipeline in order to achieve greater benefit from increasing lengths of data assimilation windows. Our code is available at https://github.com/anthony-frion/Stochastic_CODA.
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