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MEDIDA for QG

Table of contents

Introduction

Key points

  • Model error discovery with interpretability and data assimilation (MEDIDA)[1]* is scaled up to geostrophic turbulence and sparse observations
  • Naive use of neural nets (NNs) as interpolator does not capture small scales due to spectral bias, failing discoveries of closed-form errors
  • Reducing this bias using random Fourier features enables NNs to represent the full range of scales, leading to successful error discoveries

Requirements

Experiments

Case 1

Case 1 is disscused here Case 1 Location

Python code

will be updated

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  • Python 100.0%