• In what ways can machine learning contribute to balancing the competing requirements of efficiency, durability, and cost in e-powertrain designs?
• How can ML-based surrogate models built from limited test data be used to improve the reliability and usefulness of the e-powertrain design?
• What are the challenges associated with this approach?
• What are the potential impacts of simplifying model parameters through dimensionality reduction on the accuracy and efficiency of e-powertrain design?
• How can generative ML-driven design tools complement or transform traditional engineering expertise in developing innovative e-powertrain components?

• In what ways can sustainable materials be integrated into advanced e-powertrain systems without compromising performance and durability?
• How might designing e-powertrain components for easier disassembly and recycling influence system performance, manufacturing processes, and overall production efficiency?
• Is remanufacturing of motors and inverters economically viable, or will rapid technological advancements limit component reuse?
• How can engineering and manufacturing teams collaborate to lead the implementation of circularity principles and transform the sustainability of e-powertrain systems and production?
• How can the integration of circular design principles in e-powertrain systems influence the broader automotive industry’s transition toward sustainability and net-zero emissions?

Presenting the results of our World Cafés: The moderators summarize the key take-aways of their World Café Round Tables on the main stage and share the main findings with all attendees.