RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{@{}c|ccc|ccc@{}}
\toprule
   & \multicolumn{3}{c|}{\textbf{Forward}} & \multicolumn{3}{c}{\textbf{Backward}}\\ \midrule
   \textbf{Resolution} & \textbf{PhiFlow (s)} & \textbf{Ours (s)} & \textbf{Speedup} & \textbf{PhiFlow (s)} & \textbf{Ours (s)} & \textbf{Speedup}\\ \midrule
   32 $\times$ 32 $\times$ 32  & 1.282 & \textbf{0.024} & \textbf{53.4$\times$} & 1.546 & \textbf{0.095} & \textbf{16.3$\times$}\\ \midrule
   40 $\times$ 40 $\times$ 40  & 1.741 & \textbf{0.039} & \textbf{44.6$\times$} & 1.983 & \textbf{0.121} & \textbf{16.4$\times$}\\ \midrule
   48 $\times$ 48 $\times$ 48  & 2.227 & \textbf{0.068} & \textbf{32.8$\times$} & 2.412 & \textbf{0.158} & \textbf{15.3$\times$}\\ \midrule
   64 $\times$ 64 $\times$ 64 & 3.145 & \textbf{0.105} & \textbf{30.0$\times$} & 4.094 & \textbf{0.301} & \textbf{13.6$\times$}\\ %\midrule
   \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Table 2 : \textbf{Time Performance.} Our method achieves one order of magnitude speedup across all resolutions compared to PhiFlow in both forward simulation and backward gradient propagation.}
\end{table}