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@@ -235,15 +235,15 @@ <h2 class="title is-3">Quantitative Results</h2>
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-        <img width="75%" src="static/images/r1_cropped_transparent.jpg" alt="Quantitatively, B2B performs orders of magnitudes better than DeepONet on two linear operator examples."/>
+        <img width="75%" src="static/images/r1_cropped_transparent.png" alt="Quantitatively, B2B performs orders of magnitudes better than DeepONet on two linear operator examples."/>
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-        <img width="75%" src="static/images/r2_cropped_transparent.jpg" alt="B2B achieves faster convergence on a 1D Darcy dataset, and better asymptotic performance on a 2D Darcy dataset."/>
+        <img width="75%" src="static/images/r2_cropped_transparent.png" alt="B2B achieves faster convergence on a 1D Darcy dataset, and better asymptotic performance on a 2D Darcy dataset."/>
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-        <img width="75%" src="static/images/r3_cropped_transparent.jpg" alt="B2B achieves better asymptotic performance on the Elastic Plate dataset, but its performance is slightly worse on the Parameterized Heat equation dataset. This heat dataset does not have an input function space, only input parameters, which explains why B2B does not outperform DeepONet."/>
+        <img width="75%" src="static/images/r3_cropped_transparent.png" alt="B2B achieves better asymptotic performance on the Elastic Plate dataset, but its performance is slightly worse on the Parameterized Heat equation dataset. This heat dataset does not have an input function space, only input parameters, which explains why B2B does not outperform DeepONet."/>
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