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research-article

Latent assimilation with implicit neural representations for unknown dynamics

Published: 17 July 2024 Publication History

Abstract

Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding of underlying mechanisms. To address these challenges, this study presents a novel assimilation framework, termed Latent Assimilation with Implicit Neural Representations (LAINR). By introducing Spherical Implicit Neural Representations (SINR) along with a data-driven uncertainty estimator of the trained neural networks, LAINR enhances efficiency in the assimilation process. Experimental results indicate that LAINR holds a certain advantage over existing methods based on AutoEncoders, both in terms of accuracy and efficiency.

Highlights

Implicit neural representations help establish a mesh-free assimilation framework.
Spherical harmonics bring theoretical guarantee for the neural network.
Uncertainty of trained model is evaluated; compatible with existing algorithms.
Effectiveness and superiority are shown on both simulated and real-world cases.

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Published In

cover image Journal of Computational Physics
Journal of Computational Physics  Volume 506, Issue C
Jun 2024
654 pages

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Academic Press Professional, Inc.

United States

Publication History

Published: 17 July 2024

Author Tags

  1. Data assimilation
  2. Implicit neural representation
  3. Spherical harmonics
  4. Unstructured data modeling
  5. Uncertainty estimation

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