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		<title>Physics-informed AI reduces errors in aneurysm flow reconstructions</title>
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		<pubDate>Tue, 20 Oct 2026 14:07:49 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[cerebrovascular]]></category>
		<category><![CDATA[cerebrovascular flow]]></category>
		<category><![CDATA[Computational fluid dynamics]]></category>
		<category><![CDATA[field]]></category>
		<category><![CDATA[flow]]></category>
		<category><![CDATA[hemodynamics]]></category>
		<category><![CDATA[intracranial aneurysms]]></category>
		<category><![CDATA[Navier–Stokes equations]]></category>
		<category><![CDATA[out-of-distribution generalization]]></category>
		<category><![CDATA[physics-informed neural networks]]></category>
		<category><![CDATA[reconstruction]]></category>
		<category><![CDATA[surrogate modeling]]></category>
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					<description><![CDATA[A physics-informed residual network reconstructed aneurysm blood-flow fields more accurately than standard machine-learning models, with its strongest gains appearing in tests involving unseen geometries, held-out flow rates and spatially separated vessel regions.]]></description>
		
		
		
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