# Multi-horizon wind speed prediction with physics-embedded state space neural operator learning

Source: [Physics : nature.com subject feeds](https://www.nature.com/articles/s41598-026-80497-y) · 2026-11-15 · Physics
Page: https://themorning.science/story/multi-horizon-wind-speed-prediction-with-physics-embedded-state-space-neural-ope-a1056a86f38cbdd3
Kind: research · Impact: low

## In brief

- A paper in a nature.com subject feed (dated 2026-11-15) describes a physics-embedded state space neural operator for multi-horizon wind speed prediction.
- The only source available is the title and link; no summary or methodological details were provided.
- No performance figures, datasets or validation results are available from the provided sources.

## Why it matters

Wind speed forecasts across several time horizons feed into wind power planning and grid operation, so methods that combine neural operators with physical constraints are of interest to researchers in atmospheric and energy science.

## Timeline

- 2026-11-15: Paper listed in nature.com subject feed

## Coverage

- [Multi-horizon wind speed prediction with physics-embedded state space neural operator learning](https://www.nature.com/articles/s41598-026-80497-y) — Physics : nature.com subject feeds, 2026-11-15

Tags: wind speed, neural operator, physics-informed machine learning, forecasting, state space models

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Brief generated by AI (claude-haiku-5-5) from the reporting listed under Coverage. Headlines belong to their publishers. The Morning Science – https://themorning.science