Sequential Monte Carlo-based fidelity selection in dynamic-data-driven adaptive multi-scale simulations (DDDAMS)

In DDDAMS paradigm, the fidelity of a complex simulation model adapts to available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. Real-time inferencing for a large-scale system may involve hundreds of sensors for various quantity of interests, which makes it a challenging task considering limited resources. In this work, a sequential Monte Carlo method (sequential Bayesian inference technique) is proposed and embedded into the simulation to enable its ideal fidelity selection given massive datasets. As dynamic information becomes available, the proposed method makes efficient inferences to determine the sources of abnormality in the system. A parallelization frame is also discussed to further reduce the number of data accesses while maintaining the accuracy of parameter estimates. A prototype DDDAMS involving the proposed algorithm has been successfully implemented for preventive maintenance and part routing scheduling in a semiconductor supply chain.