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-driven methods to characterize demand-side flexibility using e.g., by using a set of coupled stochastics differential equations or data-driven digital twins. As a result, we will aim at obtaining a
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. Your work will focus on developing physics-informed AI methods to enhance decision-making in design and operation of next generation thermal energy storage systems, such as latent heat TES and
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during training, an effect attributed to the properties of the optimization technique. Intuitively, stochastic optimizers tend to converge to flatter minima in the complex loss landscape, which is believed
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