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Found 4 papers, 0 papers with code
Deep Orthogonal Decompositions for Convective Nowcasting
Near-term prediction of the structured spatio-temporal processes driving our climate is of profound importance to the safety and well-being of millions, but the prounced nonlinear convection of these processes make a complete mechanistic description even of the short-term dynamics challenging.
Variational Autoencoding of PDE Inverse Problems
Specifying a governing physical model in the presence of missing physics and recovering its parameters are two intertwined and fundamental problems in science.
Approximate Inference for Multiplicative Latent Force Models
Latent force models are a class of hybrid models for dynamic systems, combining simple mechanistic models with flexible Gaussian process (GP) perturbations.
Multiplicative Latent Force Models
Bayesian modelling of dynamic systems must achieve a compromise between providing a complete mechanistic specification of the process while retaining the flexibility to handle those situations in which data is sparse relative to model complexity, or a full specification is hard to motivate.
