Unlocking Energy Efficiency Through Physics-Informed AI
Save the date! Registration will be open soon for the AI-Driven Energy Efficiency in Dynamic Systems (AI-DEEDS) Workshop.
Serving as a nexus for domain experts, practitioners, and ML/AI developers to advance innovation at this critical intersection, the AI-DEEDS workshop will address the complexities of dynamic systems in engineering and science by fostering a vibrant research community and introducing transformative tools and frameworks.
Through engaging keynote speeches and invited talks, as well as contributed presentations, this workshop will not only explore cutting-edge research, but also create actionable pathways for reshaping how ML tackles the pressing challenges of energy efficiency. The introduction of the Common Task Framework (CTF) aims to establish a shared, objective foundation for testing and comparing algorithms, enabling cross-disciplinary collaboration and impactful progress.
Workshop Highlights
- Developing a unified framework: Build a taxonomy of architectures for modeling dynamic systems and foster collaboration across disciplines.
- Enabling open collaboration: Share datasets, insights, and methodologies to drive impactful solutions in energy efficiency.
- Learning from thought leaders: Hear from interdisciplinary domain experts, such as Nathan Kutz, John Sipple, Michael Kane, Hod Lipson, and Chris Develder.
- Introducing the Common Task Framework (CTF): Explore this open competition platform for benchmarking algorithms on curated dynamic system datasets that sets a standard for comparing different approaches and works towards closing the gap between theoretical advancements and real-world applications.
- Tackling real-world challenges: Participate in a CTF challenge on predicting HVAC dynamics, leveraging a dataset from commercial office buildings to address critical issues in environmental sustainability.
Learn more about the AI-DEEDS workshop.
