This track focuses on the latest developments in smart materials specifically designed for energy harvesting applications. Researchers are invited to present innovative materials and their performance in energy conversion processes.

This session explores the role of nanostructured materials in improving energy efficiency across various applications. Contributions should highlight the synthesis, characterization, and performance of these materials in energy systems.

This track emphasizes the use of predictive modeling techniques to optimize the performance of smart materials. Papers should discuss methodologies such as supervised and unsupervised learning in the context of material behavior and energy harvesting.

This session invites contributions that leverage deep learning techniques for the analysis and development of smart materials. Topics may include model training, feature extraction, and performance evaluation in energy harvesting scenarios.

This track addresses the challenges of anomaly detection in systems utilizing smart materials for energy harvesting. Researchers are encouraged to present novel approaches and case studies demonstrating effective monitoring and diagnostics.

This session focuses on the automation of workflows in the manufacturing of smart materials. Contributions should discuss techniques that enhance efficiency and quality in the production process, including additive manufacturing.

This track explores the integration of smart materials within the framework of Industrial IoT. Papers should examine the implications of device connectivity and data analytics on the performance and reliability of energy harvesting systems.

This session is dedicated to the performance analysis of piezoelectric materials used in energy harvesting applications. Researchers are invited to present experimental and theoretical studies that evaluate the efficiency and durability of these materials.

This track focuses on the simulation and modeling of smart material systems for energy harvesting. Contributions should highlight computational techniques and their applications in predicting material behavior under various conditions.

This session addresses process optimization strategies for enhancing the performance of smart materials in energy harvesting. Researchers are encouraged to present methodologies that lead to improved efficiency and cost-effectiveness.

This track emphasizes the role of analytics and data-driven approaches in the research and development of smart materials. Contributions should focus on how data can inform design choices and optimize material performance in energy systems.