Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

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Publications associées (28)

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A common method for creating compliant electrodes for dielectric elastomer actuators (DEAs) and soft sensors is to incorporate electrically conductive carbon particles into a polymer matrix. However, using unidirectional aligned carbon fibers instead not o ...

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Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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Dielectric elastomer actuators (DEAs) have raised interest due to their remarkable capabilities in various applications, such as soft robotics, haptic feedback systems, and biomedical devices. To harness the full potential of DEAs, the choice of the electr ...

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Partial discharge localization in power transformer tanks using machine learning methods

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Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

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Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood F ...

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

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Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Julien René Pierre Fageot, Adrien Raphaël Depeursinge, Daniel Abler

Nature Portfolio2024

On the Generalization of Stochastic Gradient Descent with Momentum

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