figures

tyler-ingebrand · Oct 14, 2024 · 66819ed · 66819ed
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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.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.jpg" 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.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.jpg" 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.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.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."/>
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@@ -287,7 +287,7 @@ <h2 class="subtitle has-text-centered">
         <h2 class="title is-3">Eigen Decomposition and Singular Value Decomposition</h2>
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-        <img width="75%" style="margin-bottom:1cm;" src="static/images/svd_cropped.png" alt="With small modifications to our algorithm, we recover the familiar ED and SVD algorithms."/>
+        <img width="75%" style="margin-bottom:1cm;" src="static/images/svd_cropped_transparent.png" alt="With small modifications to our algorithm, we recover the familiar ED and SVD algorithms."/>
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diff --git a/static/images/r3_cropped.jpg b/static/images/r3_cropped.jpg
diff --git a/static/images/r3_cropped_transparent.png b/static/images/r3_cropped_transparent.png
diff --git a/static/images/svd_cropped.png b/static/images/svd_cropped.png
diff --git a/static/images/svd_cropped_transparent.png b/static/images/svd_cropped_transparent.png