Minor decorative touch-up to blog post "The Well"

Author: ceciliasnho

_posts/2025-02-05-thewell.md

@@ -20,17 +20,16 @@ In contrast, scientific data presents unique challenges — it is harder to gath
 
 Despite these difficulties, datasets for modeling physical dynamics are expanding. While fluid-dynamics simulations have gained traction as common benchmarks, they’ve been addressing only a limited range of physics or offering a sparse number of high-resolution snapshots. Additionally, the size and complexity of individual samples often constrain their broader utility. These limitations underscore the need for new datasets tailored and scaled for modern machine learning use. This led us to create The Well, a unified collection of diverse physical processes, readily usable to train neural network surrogates at scale.
 
+<p align="center">
+  <img src="/images/blog/thewell1.jpg" alt="The Well" width="95%" style="mix-blend-mode: darken;">
+</p>
 
 #### What is The Well?
 
 The Well comprises 16 datasets totaling over 15TB, with individual sizes ranging from 6.9GB to 5.1TB. All data is provided on uniform spatial grids sampled at constant time intervals and formatted in the `HDF5` format for simplicity, accessibility, and compatibility with scientific workflows. To facilitate usage, we also provide a PyTorch interface for a seamless integration with machine learning models.
 
 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.