High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling (opens in new tab)

BIAS (Bayesian Inference with Accelerated Sampling) is a high-throughput parameter estimation framework designed to rapidly infer the root causes of device underperformance in real time. It integrates a deep neural network surrogate model with accelerated Markov Chain Monte Carlo sampling to efficiently explore high-dimensional parameter spaces and identify needle-like regions corresponding to the ground truth values of key physical parameters. BIAS is scalable to complex systems and has been...