PhysMetrics.Weather is an evaluation framework that quantifies physical realism of ML weather prediction models using conservation, spectral, and dynamical metrics.
and Johnson, Donald R
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Derives compactly-supported nonstationary kernel enabling exact GPs on million-point datasets, shown on space-time temperature prediction.
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.
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PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
PhysMetrics.Weather is an evaluation framework that quantifies physical realism of ML weather prediction models using conservation, spectral, and dynamical metrics.
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Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Derives compactly-supported nonstationary kernel enabling exact GPs on million-point datasets, shown on space-time temperature prediction.
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Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.