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Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human cognitive limits by involving too many features, while probabilistic relaxations have remained largely limited to categorical classification. We present a unified framewor...
A novel deep learning surrogate pipeline based on the Swin3D Transformer is introduced to predict spatiotemporal discharge dynamics directly from volumetric data, providing a scalable and efficient framework for high-throughput battery design and optimization.
The widespread adoption of artificial intelligence (AI) within real-world applications has raised a lot of concerns regarding their trustworthiness, especially in critical applications. The field of eXplainable AI (XAI) has emerged with the objective of providing explanations to the users about the decisions made by AI...
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