About This Episode
Today we’re joined by Rose Yu, an assistant professor at the Jacobs School of Engineering at UC San Diego.
Rose’s research focuses on advancing machine learning algorithms and methods for analyzing large-scale time-series and spatial-temporal data, then applying those developments to climate, transportation, and other physical sciences. We discuss how Rose incorporates physical knowledge and partial differential equations in these use cases and how symmetries are being exploited. We also explore their novel neural network design that is focused on non-traditional convolution operators and allows for general symmetry, how we get from these representations to the network architectures that she has developed and another recent paper on deep spatio-temporal models.
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- Paper: Trajectory Prediction using Equivariant Continuous Convolution
- Paper: Symmetry in Deep Dynamics Models
- Paper: Physics-Guided Deep Learning for Fluid Dynamics
- Paper: Quantifying Uncertainty in Deep Spatiotemporal Forecasting
- Paper: Towards Physics-informed Deep Learning for Turbulent Flow Prediction
- Research Overview