mlsanityData to 14 Oct 2026

M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts

arXiv 2304.09070 ↗First posted 18 Apr 2023physics.ao-ph cs.LG cs.NAcited by 3

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Abstract (arXiv)

In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here, we ask the question of how these numerical forecasts would have turned out, if machine learning based solvers had been used instead of standard numerical discretizations. Specifically, we recreate these numerical forecasts using physics-informed neural networks. We show that physics-informed neural networks provide an easier and more accurate methodology for solving meteorological equations on the sphere, as compared to the ENIAC solver.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 18 Apr 2023.

Builds on

13 of 39 references.

References · 13

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  7. 2022Respecting causality is all you need for training physics-informed neural networkscited by 156
  8. 2021Long-time integration of parametric evolution equations with physics-informed DeepONetscited by 89
  9. 2023A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionscited by 48
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  11. 2023Improving physics-informed neural networks with meta-learned optimizationcited by 19
  12. 2020A generative adversarial network approach to (ensemble) weather predictioncited by 18
1 more
  1. 2022Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networkscited by 5

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Improving physics-informed DeepONets with hard constraintscited by 9
  2. 2023Towards replacing precipitation ensemble predictions systems using machine learningcited by 1
  3. 2025Low-rank adaptive physics-informed HyperDeepONets for solving differential equationscited by 0