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		<title>Hopf-inspired generative adversarial network augments univariate time series with bifurcation dynamics</title>
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		<pubDate>Mon, 26 Oct 2026 18:02:00 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[dynamical systems]]></category>
		<category><![CDATA[ECG]]></category>
		<category><![CDATA[Fréchet distance]]></category>
		<category><![CDATA[generative adversarial networks]]></category>
		<category><![CDATA[Hopf bifurcation]]></category>
		<category><![CDATA[Hopf-inspired]]></category>
		<category><![CDATA[HopfGAN]]></category>
		<category><![CDATA[Lyapunov stability]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[maximum mean discrepancy]]></category>
		<category><![CDATA[synthetic data]]></category>
		<category><![CDATA[Time Series Augmentation]]></category>
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					<description><![CDATA[A new generative adversarial framework embeds Hopf bifurcation dynamics and Lyapunov-based regularization into its generator, reporting improved distributional similarity, temporal coherence, and training stability across diverse univariate time-series benchmarks.]]></description>
		
		
		
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