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	<title>bidirectional LSTM &#8211; BIOENGINEER.ORG</title>
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		<title>Physics Meets AI: New Machine Learning Framework Predicts How Long Electric Vehicle Drive Systems Will Last</title>
		<link>https://bioengineer.org/physics-meets-ai-new-machine-learning-framework-predicts-how-long-electric-vehicle-drive-systems-will-last/</link>
		
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		<pubDate>Thu, 08 Oct 2026 23:02:38 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[accelerated durability testing]]></category>
		<category><![CDATA[Attention Mechanism]]></category>
		<category><![CDATA[Bayesian Optimization]]></category>
		<category><![CDATA[bidirectional LSTM]]></category>
		<category><![CDATA[convolutional neural network]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[electric vehicle drive system]]></category>
		<category><![CDATA[Physics-Informed Machine Learning]]></category>
		<category><![CDATA[Predictive Maintenance]]></category>
		<category><![CDATA[Remaining useful life prediction]]></category>
		<category><![CDATA[sparse autoencoder]]></category>
		<category><![CDATA[uncertainty quantification]]></category>
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					<description><![CDATA[Researchers have developed a physics-informed machine learning framework that predicts the remaining useful life of electric vehicle drive systems with errors under five percent while quantifying uncertainty.]]></description>
		
		
		
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