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docs/plaid_benchmarks.md

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@@ -7,8 +7,8 @@ We provide interactive benchmarks hosted on Hugging Face, in which
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anyone can test their own SciML method. These benchmarks involve
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regression problems posed on datasets provided in PLAID format. Some of
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these datasets have been introduced in the MMGP (Mesh Morphing Gaussian
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Process) paper `casenave2023mmgp`, and the PLAID paper
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`casenave2025plaid`. A ranking is automatically updated based on a score
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Process) paper @casenave2023mmgp, and the PLAID paper
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@casenave2025plaid. A ranking is automatically updated based on a score
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computed on the testing set of each dataset. For the benchmarks to be
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meaningful, the outputs on the testing sets are not made public.
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@@ -18,13 +18,24 @@ $\{ \mathbf{U}^i_{\rm pred} \}_{i=1}^{n_\star}$ be the test observations
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and predictions, respectively, of a given field of interest. The
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relative RMSE is defined as
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$$\mathrm{RRMSE}_f(\mathbf{U}_{\rm ref}, \mathbf{U}_{\rm pred}) = \left( \frac{1}{n_\star}\sum_{i=1}^{n_\star} \frac{\frac{1}{N^i}\|\mathbf{U}^i_{\rm ref} - \mathbf{U}^i_{\rm pred}\|_2^2}{\|\mathbf{U}^i_{\rm ref}\|_\infty^2} \right)^{1/2},$$
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Inline: $E = mc^2$
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Block:
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$$
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\int_0^1 x^2 dx = \frac{1}{3}
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$$
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$$
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\mathrm{RRMSE}_f(\mathbf{U}_{\rm ref}, \mathbf{U}_{\rm pred}) = \left( \frac{1}{n_\star}\sum_{i=1}^{n_\star} \frac{\frac{1}{N^i}\|\mathbf{U}^i_{\rm ref} - \mathbf{U}^i_{\rm pred}\|_2^2}{\|\mathbf{U}^i_{\rm ref}\|_\infty^2} \right)^{1/2},
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$$
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where $N^i$ is the number of nodes in the mesh $i$, and
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$\max(\mathbf{U}^i_{\rm ref})$ is the maximum entry in the vector
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$\mathbf{U}^i_{\rm ref}$. Similarly for scalar outputs:
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$$\mathrm{RRMSE}_s(\mathbf{w}_{\rm ref}, \mathbf{w}_{\rm pred}) = \left( \frac{1}{n_\star} \sum_{i=1}^{n_\star} \frac{|w^i_{\rm ref} - w_{\rm pred}^i|^2}{|w^i_{\rm ref}|^2} \right)^{1/2}.$$
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$$
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\mathrm{RRMSE}_s(\mathbf{w}_{\rm ref}, \mathbf{w}_{\rm pred}) = \left( \frac{1}{n_\star} \sum_{i=1}^{n_\star} \frac{|w^i_{\rm ref} - w_{\rm pred}^i|^2}{|w^i_{\rm ref}|^2} \right)^{1/2}.
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$$
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## Resources
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</div>
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## References
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[^bib]
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```bibtex

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