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<oembed><version>1.0</version><provider_name>Inverse Problems Research Group</provider_name><provider_url>https://sites.uef.fi/inverse</provider_url><title>Uncertainty Quantification - Inverse Problems Research Group</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="3fQ2UhnXQO"&gt;&lt;a href="https://sites.uef.fi/inverse/research-areas/uncertainty-quantification/"&gt;Uncertainty Quantification&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://sites.uef.fi/inverse/research-areas/uncertainty-quantification/embed/#?secret=3fQ2UhnXQO" width="600" height="338" title="&#x201C;Uncertainty Quantification&#x201D; &#x2014; Inverse Problems Research Group" data-secret="3fQ2UhnXQO" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Uncertainty Quantification Quantities of interest cannot always be directly measured, but they can be linked to measurable quantities via models. In practice, both models and data suffer from inaccuracies and uncertainties. Minor inaccuracies in the models can sometimes be handled by traditional deterministic methods. However, such methods cause meaningless predictions and ill-advised decisions if model [&hellip;]</description></oembed>

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