Probabilistic and Robust Engineering Design

Probabilistic and Robust Engineering Design is a research topic within Statistics, Probability and Uncertainty. Science Explorer counts 47k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

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.

• Uncertainty Quantification
• Sensitivity Analysis
• Polynomial Chaos
• Monte Carlo Simulation
• Reliability Analysis
• Stochastic Differential Equations
• Global Sensitivity Indices
• Sparse Grids
• Latin Hypercube Sampling
• Probabilistic Design Optimization