add news and publications

content/news/2607AGU.md

@@ -0,0 +1,22 @@
+---
+date: 2026-07-01T09:29:16+10:00
+title: " AGU 2026 — Save the Date & Submit Your Abstracts!"
+heroHeading: ''
+heroSubHeading: ''
+heroBackground: ''
+thumbnail:  'images/news/2607AGU.png'
+images: ['images/news/2607AGU.png']
+link: 'https://agu.confex.com/agu/agu26/prelim.cgi/Home/0'
+---
+
+AGU26 is coming to San Francisco, December 7–11, and abstract submissions are open until **August 5th**. We encourage the community to consider submitting to the following **sessions co-convened by M²LInES members**:
+
+- Developments in Machine Learning Across Earth System Modeling: Subgrid-Scale Parameterizations, Emulation, and Hybrid Modeling (co-convened by **Sara Shamekh**)
+
+- Subseasonal to Seasonal Tropical Variability: Observations, Modeling, Processes, and Global Impacts (co-convened by **Danni Du**)
+
+- Emerging Machine Learning Approaches for Ecosystem Process Understanding and Knowledge Discovery (co-convened by **Pierre Gentine**)
+
+- Climate Tipping Points and their Impacts (co-convened by **Pierre Gentine**)
+
+**Submit your abstract [here](https://agu.confex.com/agu/agu26/prelim.cgi/Home/0) by August 5th 23:59pm EDT**

content/news/2607Ars.md

@@ -0,0 +1,12 @@
+---
+date: 2026-07-01T09:29:16+10:00
+title: " M²LInES in the News: Ars Technica article"
+heroHeading: ''
+heroSubHeading: ''
+heroBackground: ''
+thumbnail:  'images/news/2607ArsTechnica.png'
+images: ['images/news/2607ArsTechnica.png']
+link: 'https://arstechnica.com/science/2026/06/the-weather-and-climate-science-ai-revolution-isnt-revolutionary/'
+---
+
+Ars Technica recently **[featured](https://arstechnica.com/science/2026/06/the-weather-and-climate-science-ai-revolution-isnt-revolutionary/)** the growing role of AI in weather and climate science, **highlighting the work of Laure Zanna and the M²LInES project** in developing physics-informed machine learning approaches for climate modeling.

content/news/2607Nasser.md

@@ -0,0 +1,12 @@
+---
+date: 2026-07-01T09:29:16+10:00
+title: " Design principles for stable and generalizable data-driven discretizations for solving linear hyperbolic conservation laws"
+heroHeading: ''
+heroSubHeading: ''
+heroBackground: ''
+thumbnail:  'images/news/2607Nasser.png'
+images: ['images/news/2607Nasser.png']
+link: 'https://doi.org/10.48550/arXiv.2606.17497'
+---
+
+Antoine Nasser and Alistair Adcroft investigate how machine learning can be used to develop **stable and accurate numerical schemes for solving the linear advection equation**. Their **[study](https://doi.org/10.48550/arXiv.2606.17497)** identifies the key factors governing the performance of data-driven finite-volume methods, including network architecture, training data, and normalization strategies. They show that data-driven reconstructions based on cell averages are shape-specific, limiting their ability to generalize across different classes of solutions. They introduce a machine-learned flux limiter that improves shape preservation relative to widely used classical schemes and demonstrate that training on polynomial profiles yields stable, high-order accurate discretizations. Overall, the work provides **practical guidelines for designing robust and generalizable machine-learning-based numerical methods for scientific computing.**

content/news/2607Samudra2.md

@@ -0,0 +1,24 @@
+---
+date: 2026-07-01T09:29:16+10:00
+title: " Samudra 2: Supercomputer Ocean Modeling on a Single GPU!"
+heroHeading: ''
+heroSubHeading: 'Samudra 2: Scaling Ocean Emulators across Resolutions'
+heroBackground: ''
+thumbnail:  'images/news/2607Samudra2.png'
+images: 
+link: 'https://doi.org/10.48550/arXiv.2606.02610'
+---
+
+Yuan Yuan and collaborators from M²LInES and Open Athena have unveiled **[Samudra 2](https://doi.org/10.48550/arXiv.2606.02610)**, a next-generation neural ocean emulator that collapses massive supercomputer workloads down to a single GPU. Running **100x to 1000x faster** than traditional numerical models, it successfully scales to fine resolutions capable of capturing critical mesoscale eddies and sharp currents like the Gulf Stream without drifting or "blowing up" over multi-year simulations.
+
+
+
+By slashing deep-ocean errors by **7–10×** and maintaining realistic physics, Samudra 2 turns a major computational bottleneck into an opportunity for massive ensemble modeling. This open-source breakthrough shifts climate science from rationing a few scenarios to running hundreds of plausible futures, paving the way for low-cost, decision-grade forecasting in shipping, energy, and sea-level rise. Read the full announcement in our **[latest blog post](https://medium.com/@lz1955/samudra-2-a-fast-cheap-ai-ocean-model-now-at-the-scale-that-matters-b37883c62d51)**, explore the **[project page and rollout demo](https://m2lines.github.io/Samudra/docs/)**, grab the **[code on GitHub](https://github.com/m2lines/Samudra)**, or just run it **using the weights on [Hugging Face](https://huggingface.co/M2LInES/Samudra2)**. 
+
+
+
+If you are interested in developing these models, [join our growing team at M²LInES](/jobs)!
+
+{{< youtube Vs8fdoVlwis >}}
+
+</br>

content/news/Newsletters/_index.md

@@ -12,6 +12,8 @@ tags:
 
 ### 2026
 
+* 07/01/2026 - [M²LInES newsletter - July 2026](https://mailchi.mp/1e6b380a8003/m2lines-july2026)
+
 * 06/02/2026 - [M²LInES newsletter - June 2026](https://mailchi.mp/25cfd79a0287/m2lines-june2026)
 
 * 05/01/2026 - [M²LInES newsletter - May 2026](https://mailchi.mp/0ea31f7e9316/m2lines-may2026)

content/publications/_index.md

@@ -14,6 +14,17 @@ You can also check all our publications on our **[Google Scholar profile](https:
 <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> M²LInES funded research
 
 ### 2026
+<div style="display: flex; align-items: center;">
+  <div style="width: 400px; height: 100px; overflow: hidden;  margin-right: -50px;">
+    <img src="/images/news/2607Nasser.png" style="width: 100px; height: 100px;">
+  </div>
+  <p>
+    <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
+    <strong>Antoine-Alexis Nasser, Alistair Adcroft</strong><br>
+    <a href="https://doi.org/10.48550/arXiv.2606.17497" target="_blank"><strong> Design principles for stable and generalizable data-driven discretizations for solving linear hyperbolic conservation laws</strong></a><br>
+    <i>GRL</i> <strong>DOI</strong>:10.48550/arXiv.2606.17497
+  </p>
+</div>
 
 <div style="display: flex; align-items: center;">
   <div style="width: 400px; height: 100px; overflow: hidden;  margin-right: -50px;">