Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms

• URL: http://arxiv.org/abs/2603.20891v1
• Date: Sat, 21 Mar 2026 17:40:06 GMT
• Title: Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
• Abstract summary: This paper introduces a framework for jointly learning the state, dynamics, and parameters of filtering algorithms in data assimilation.<n>We demonstrate how several well-known data assimilation methods can be learned or tuned within this framework.
• Score: 10.144640509287187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data assimilation algorithms estimate the state of a dynamical system from partial observations, where the successful performance of these algorithms hinges on costly parameter tuning and on employing an accurate model for the dynamics. This paper introduces a framework for jointly learning the state, dynamics, and parameters of filtering algorithms in data assimilation through a process we refer to as auto-differentiable filtering. The framework leverages a theoretically motivated loss function that enables learning from partial, noisy observations via gradient-based optimization using auto-differentiation. We further demonstrate how several well-known data assimilation methods can be learned or tuned within this framework. To underscore the versatility of auto-differentiable filtering, we perform experiments on dynamical systems spanning multiple scientific domains, such as the Clohessy-Wiltshire equations from aerospace engineering, the Lorenz-96 system from atmospheric science, and the generalized Lotka-Volterra equations from systems biology. Finally, we provide guidelines for practitioners to customize our framework according to their observation model, accuracy requirements, and computational budget.

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