research Bayesian Optimal Experimental Design Designing experiments for inference and decisions under uncertainty Neural Operator Accelerated BOED Making BOED scalable for expensive PDE-governed systems Robust and Decision-Focused BOED Designing experiments that remain useful when priors are imperfect and decisions matter Generative Priors for High-Dimensional BOED Pretrained diffusion models as flexible priors for scalable BOED in non-Gaussian, high-dimensional inverse problems LLM-Elicited Priors for Model Predictive Control Turning domain knowledge in language models into structured priors for decision-making under data scarcity
