Title: Living Scenes: Creating and updating digital twins of evolving indoor scenes Abstract Buildings are like living organisms, i.e., they evolve over time due to interaction with natural phenomena and humans. How can we realistically maintain their digital twins throughout their lifespan? Or else, how can we maintain a living building model as the space is […]
TrAC ADLRG Mini-course on Federated Learning (class 1/3) In this mini-course, we will cover concepts in Federated Learning.
Title: Unified Training of Universal Time Series Forecasting Transformers
Title: Transforming AI Practices: From Data Generation to Automated MLOps Abstract Agility enables swift adaptation to changing market needs and new technologies. Moreover, mature AI utilization necessitates a strategic shift, focusing on incorporating AI into core business operations to boost efficiency and drive innovation. Increasing agility and harnessing the power of mature AI, this presentation […]
TrAC ADLRG Mini-course on Federated Learning (class 2/3) In this mini-course, we will cover concepts in Federated Learning.
Title: Operator learning for data-driven closure models of complex dynamical systems Abstract Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and Earth’s climate, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale separation, deterministic and local closure models […]
TrAC ADLRG Mini-course on Federated Learning (class 3/3) In this mini-course, we will cover concepts in Federated Learning.
Welcome to the TrAC Bootcamp on "Deep Dive into AI" Get ready for a hands-on, four-day journey into the world of Artificial Intelligence at our upcoming TrAC Bootcamp. Whether you're a student, professional, or just curious about AI, this bootcamp is designed to give you a comprehensive introduction to the field. Dates: June 4-7, 2024Time: […]
Course Description Dive into the theory, implementation, and limitations of Scientific Machine Learning (SciML) models. In this course, you will engage in hands-on and practical activities under the expert guidance of our experienced instructors. Learning SciML will be enjoyable and rewarding as you engage in real-world applications, such as solving partial differential equations using physics-informed […]
