Artificial intelligence (AI) is transforming science and engineering, with neural networks (NNs) at the forefront of breakthroughs in fields such as natural language processing, computer vision, and generative media. In parallel, lithium-ion and emerging battery technologies are being deployed on a massive scale to power electric transportation, store renewable energy, and support a low-carbon future. These trends converge in a critical research area: the use of advanced AI methods to enhance battery performance, reliability, and safety.

This course provides a focused introduction to neural networks—ranging from foundational architectures to advanced deep neural networks (DNNs) and physics-informed neural networks (PINNs)with a special emphasis on their applications in battery applications. Participants will learn how these techniques can be used for battery state estimation, lifetime prediction, and safety management.

For additional information, updates, and registration, please refer to AAU PhDMoodle via the link provided on the right side of this page.

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