4 papers
Measure Neural Mappings
Ricardo Baptista, Edoardo Calvello, Bohan Chen +2
We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operators on probability measures. E…
Learning Probabilistic Filters with Strictly Proper Scoring Rules
Eviatar Bach, Ricardo Baptista, Jochen Bröcker +2
Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system given observations, i…
Flow Matching for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao +1
Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (EnSF) improve DA in high-dimensional n…
Learning Enhanced Ensemble Filters
Eviatar Bach, Ricardo Baptista, Edoardo Calvello +2
The filtering distribution in hidden Markov models evolves according to the law of a mean-field model in state-observation space. The ensemble Kalman filter (EnKF) approximates thi…