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| author | mjkwiatkowski <mati.rewa@gmail.com> | 2026-06-03 14:48:59 +0200 |
|---|---|---|
| committer | mjkwiatkowski <mati.rewa@gmail.com> | 2026-06-03 14:48:59 +0200 |
| commit | 56ad0caa9f6d1e1248749d66ec21b64f51cff293 (patch) | |
| tree | 023f6ffc424853f4f3c6215ef105d5d3b004bff0 /content | |
| parent | 304ccf591c2eb83458e4d34c06f04643b39df141 (diff) | |
feat: minor changes to few sections
Diffstat (limited to 'content')
| -rw-r--r-- | content/background.tex | 4 |
1 files changed, 4 insertions, 0 deletions
diff --git a/content/background.tex b/content/background.tex index fc1fce7..4218380 100644 --- a/content/background.tex +++ b/content/background.tex @@ -47,6 +47,10 @@ The digital twin is designed to provide extra datasets for training \gls{ai} mod DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} is an adaptive digital twin with visualization and anomaly detection features. +% What is more, Microsoft already offers digital twinning as a service https://azure.microsoft.com/en-us/products/digital-twins/ +% Documentation: https://learn.microsoft.com/en-us/azure/digital-twins/ +% Moreover, NVIDIA is doing too as well https://www.nvidia.com/en-sg/omniverse/ + Predictive modelling uses statistics to predict outcomes. When deployed commercially, for example in datacenters, predictive modelling is often referred to as predictive analytics~\cite{Wikipedia:PredictiveModelling}. Almost any statistical model can be used for prediction purposes, but nowadays predictive analysis is synonymous with machine learning. |