Probabilistic and Robust Engineering Design -- 专题文献

专题：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.

专题：Probabilistic and Robust Engineering Design

最新文献

Data-driven reliability-based optimization framework for concrete-encased steel composite columns toward cost-effective design: Methodology and interactive tool

Variational Bayesian inference-assisted approach for multi-objective reliability-based design optimization

Generative data-physics fusion for hierarchical reliability assessment

A level-set-oriented and problem-independent robust topology optimization strategy based on convolutional neural networks and uncertainty clustering

Uncertainty Quantification for In Silico Chemistry

A novel reliability-oriented concurrent topology optimization framework based on improved sequential decoupling and interval-modeled boundary modification strategies

Direct probability integral assisted machine learning framework for uncertain vibration response of porous bi-directional functionally graded sandwich structures

Reliability in Robotics and Intelligent Systems: Mathematical Modeling and Algorithmic Innovations

Interval theory-embedded data-driven identification framework for uncertain thermo-elastic parameters

Reliability-based design optimization of a mooring system for a floating wind turbine

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