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Obiabanaki · Jan 21, 2018 · d387e3c · d387e3c
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diff --git a/docs/_config.yml b/docs/_config.yml
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 theme: jekyll-theme-cayman
 title: Physics Informed Neural Networks
-description: Deap Learning of Nonlinear Partial Differential Equations
 description: Deep Learning of Nonlinear Partial Differential Equations
diff --git a/docs/index.md b/docs/index.md
@@ -91,12  91,7 @@ The following figure summarizes our results for the data-driven solution of the
 
 ![](http://www.dam.brown.edu/people/mraissi/assets/img/Burgers_CT_inference.png)
 
-<div class="img_row">
-    <img class="col three" src="{{ site.url }}/assets/img/Burgers_CT_inference.png" alt="" title="Burgers_CT_inference"/>
-</div>
-<div class="col three caption">
-    Burgers' equation: Top: Predicted solution along with the initial and boundary training data. In addition we are using 10,000 collocation points generated using a Latin Hypercube Sampling strategy. Bottom: Comparison of the predicted and exact solutions corresponding to the three temporal snapshots depicted by the white vertical lines in the top panel. Model training took approximately 60 seconds on a single NVIDIA Titan X GPU card.
-</div>
 > Burgers' equation: Top: Predicted solution along with the initial and boundary training data. In addition we are using 10,000 collocation points generated using a Latin Hypercube Sampling strategy. Bottom: Comparison of the predicted and exact solutions corresponding to the three temporal snapshots depicted by the white vertical lines in the top panel. Model training took approximately 60 seconds on a single NVIDIA Titan X GPU card.
 
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