From 5f37feffea1773ad0a08da3fc5f193cc37f1013b Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Thu, 25 Jun 2026 15:25:18 +0200
Subject: feat: added the evaluation and validation notes, only experiment
 pictures are now missing

---
 main.tex | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'main.tex')

diff --git a/main.tex b/main.tex
index 2881570..2ff5706 100644
--- a/main.tex
+++ b/main.tex
@@ -158,10 +158,9 @@
 	%If we had that in, we can reach these kinds of gains.
 	% @Mateusz there is really not a possibility to incorporate CINECA's models, so to address Dante's feedback, I created this experiment.
 
-	\begin{tcolorbox}[title=Failure Detection: Main Finding I]
-		On average, \emph{Sunfish} can detect 14.5\% of unexpected failures in the physical twin.
-		We show, that digital twinning \emph{can} be used for failure detection.
-
+	\begin{tcolorbox}[title=Validation]
+		We posit digital twinning can be used for failure detection to the benefit of DC operators.
+		We validate our system against DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} designed by Milojicic \etal to show we achieve similar results.
 	\end{tcolorbox}
 	\begin{minipage}[b]{0.45\linewidth}
 		\begin{center}
@@ -178,8 +177,9 @@
 \end{frame}
 
 \begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II}
-	\begin{tcolorbox}[title=Scheduling Optimization: Main Finding II]
-		Here explain what did you find.
+	\begin{tcolorbox}[title=Evaluation]
+		Predictive analytics is core to digital twinning. We evaluate our system against the requirements (extra slides) by predicting an optimal scheduling policy.
+		During runtime, we make dynamic adjustments to the physical twin, if the scheduling results differ.
 	\end{tcolorbox}
 
 \end{frame}
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