2 papers
cs.IT2026
Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
Huchen Yang, Xinghao Dong, Jinlong Wu
Bayesian experimental design (BED) for complex physical systems is often limited by the nested inference required to estimate the expected information gain (EIG) or its gradients.…
cs.LG2025
Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network
Chuanqi Chen, Zhongrui Wang, Nan Chen +1
A discrete-time conditional Gaussian Koopman network (CGKN) is developed in this work to learn surrogate models that can perform efficient state forecast and data assimilation (DA)…