Data-Driven Model Reduction, Scientific Frontiers, and Applications ()
- Texas A&M University
- College Station, TX
- Joe C. Richardson Petroleum Engineering Building (RICH) 910
- Arash Noshadravan, Zachry Department of Civil Engineering
- Model Reduction and Decision Making Under Uncertainty for Optimal Management of Infrastructures
Identifying the optimal design, maintenance, and mitigation strategies for enhancing sustainability and resilience of infrastructures relies on accurate predictive models of their lifetime performance under future progressive and sudden deterioration. Advances in computational simulation can potentially lead to a paradigm shift in more informed-decision making for urban scale infrastructure management. However, a challenge resides in high computational cost in propagation of various sources of uncertainty through the system when estimating relevant performance metrics. In this presentation, we provide a few examples of uncertainty-aware surrogate modeling and model reduction in the context of infrastructure system performance evaluation and optimization. This includes risk-informed hurricane-induced loss assessment at urban scale, reliability-based corrosion mitigation of underground pipelines, and streamlined mitigation and planning of city-scale energy consumption.