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	<title>Extreme Learning Machine &#8211; BIOENGINEER.ORG</title>
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		<title>Neural Networks Taught to Respect Physics Even Without Equations</title>
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		<pubDate>Mon, 05 Oct 2026 19:14:41 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Extreme Learning Machine]]></category>
		<category><![CDATA[feedforward neural networks]]></category>
		<category><![CDATA[inductive bias]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[model regularization]]></category>
		<category><![CDATA[physics-informed neural networks]]></category>
		<category><![CDATA[QSAR toxicity]]></category>
		<category><![CDATA[qualitative consistency]]></category>
		<category><![CDATA[regression]]></category>
		<category><![CDATA[static gain constraints]]></category>
		<category><![CDATA[UCI benchmarks]]></category>
		<category><![CDATA[weight initialization]]></category>
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					<description><![CDATA[Researchers have shown that imposing hard constraints on the direction of input-output effects during neural network training guarantees physically consistent regression models even when no equations describing the phenomenon exist.]]></description>
		
		
		
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