Probabilistic and Robust Engineering Design
This cluster of papers focuses on uncertainty quantification and sensitivity analysis in complex mathematical and computational models. It explores methods such as polynomial chaos, Monte Carlo simulation, and sparse grids to assess and manage uncertainties in various engineering and scientific applications. The research also delves into topics like global sensitivity indices, reliability analysis, stochastic differential equations, and probabilistic design optimization.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.