Decorative touch-up to the blog post "The Well"

Author: ceciliasnho

_posts/2025-02-05-thewell.md

@@ -1,14 +1,14 @@
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 layout: post
 title: "Bridging the Gap Between Physical Numerical Simulations and Machine Learning: Introducing The Well"
-authors: Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina J. Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam H. Parker, Miles Cranmer, Shirley Ho
+authors: Ruben Ohana*, Michael McCabe*, Lucas Meyer, Rudy Morel, Fruzsina J. Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam H. Parker, Miles Cranmer, Shirley Ho
 shorttitle: "The Well"
 date: 2024-12-03 12:00
-smallimage: thewell.jpg
-image: thewell.jpg
+smallimage: the_well_gif.gif
+image: the_well_gif.gif
 blurb: We release The Well, a large-scale collection of physics numerical simulations created with domain experts and formatted for a machine learning usage.
 shortblurb: We release The Well, a large-scale collection of physics numerical simulations created with domain experts and formatted for a machine learning usage.
-splashimage: /images/blog/thewell.jpg
+splashimage: /images/blog/the_well_gif.gif
 link: https://arxiv.org/abs/2412.00568
 github_link: https://github.com/PolymathicAI/the_well
 permalink: /blog/thewell/
@@ -27,6 +27,10 @@ The Well comprises 16 datasets totaling over 15TB, with individual sizes ranging
 
 We collaborated closely with domain experts to generate and curate datasets representing complex physical phenomena and standardized them into a unified format. This approach ensures that datasets are self-sufficient, easily shareable, and ready for direct application to machine learning models, eliminating preprocessing overhead. By prioritizing usability, we allow researchers to focus on the true challenge: predicting the physics.
 
+<p align="center">
+  <img src="/images/blog/thewell1.jpg" alt="The Well" width="95%" style="mix-blend-mode: darken;">
+</p>
+
 #### Opportunities for the Numerical Simulation Community
 
 Through conversations with experts in numerical simulations, we identified a significant communication gap between their field and the machine learning community. This disconnect, inflated by the hype surrounding AI, often leads to skepticism about what machine learning can truly accomplish. With The Well, we aim to make a first step toward bridging this gap, by offering a platform that encourages collaboration while providing challenging datasets that represent advanced and, sometimes, poorly understood physical processes.