PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
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Hierarchical DMD decomposition of Antarctic sea ice data separates interannual variability from an emerging climate trend and supports two-year forecasts via a regularized predictive model (IceDMD) that outperforms existing approaches.
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PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
PiGGO integrates a learned graph neural ODE as the continuous-time dynamics model within an extended Kalman filter to enable online virtual sensing and uncertainty-aware state estimation for nonlinear dynamic systems with unknown model form and sparse sensing.
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Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
Hierarchical DMD decomposition of Antarctic sea ice data separates interannual variability from an emerging climate trend and supports two-year forecasts via a regularized predictive model (IceDMD) that outperforms existing approaches.