# About Jua Jua's EPT (Earth Physics Transformer) weather model learns historical weather patterns and fundamental atmospheric physics principles through its end-to-end transformer architecture. It is trained on public and proprietary observed data and physics-based simulations, such as ERA5. This dual approach allows it to capture complex atmospheric dynamics and physical laws while maintaining rapid processing speeds — unlike traditional NWP models, which solve physical equations sequentially by dividing the atmosphere into 3D grid cells and requiring precise initial conditions. Further reading: [EPT-2 Launch Announcement](https://jua.ai/company/blog/ept-2-launch-announcement) & [EPT-2 Technical Report](https://arxiv.org/abs/2507.09703) ### Key differences vs. traditional numerical modelling End-to-end AI models learn both historical weather patterns and atmospheric physics through advanced neural networks trained on observed data and physics-based simulations. Jua's models can predict weather events outside their training dataset by learning fundamental physics. Processing raw observational input directly into forecasts provides key advantages: 1. **Accuracy** - Generate accurate forecasts for hyper-local predictions 2. **Resource efficiency** - Less computational power needed during inference 3. **Adaptation capability** - Quick incorporation of new data patterns 4. **Resolution flexibility** - Seamless scaling across different resolutions 5. **Uncertainty handling** - Direct probability distribution outputs ### Why does it matter for energy trading? Our AI weather model's speed and accuracy directly impact energy trading decisions by: 1. Enabling faster reactions to weather shifts that affect renewable generation and demand 2. Adapting quickly to emerging weather patterns that influence market pricing 3. Delivering consistent accuracy across multiple forecast horizons critical for different trading products ### Ready to get started? Dive into our [getting started guide](/api-v2/getting-started.md) now! --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.jua.ai/introduction/about-jua.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.