Simulation-based Probabilistic Risk Assessment
Simulation-based probabilistic risk assessment (SPRA) is a systematic and comprehensive methodology that has been used and refined over the past few decades to evaluate the risks associated with complex systems. SPRA models are well established for cases with considerable data and system behavior information available. In this regard, multiple statistical and probabilistic tools can be used to provide a valuable assessment of dynamic probabilistic risk levels in different applications. This tutorial presents a comprehensive review of SPRA methodologies. Based on the reviewed literature, SPRA methods can be classified into three categories of dynamic probabilistic logic methods, dynamic stochastic analytical models, and hybrid discrete dynamic event and system simulation models. In this tutorial, the key strengths and weaknesses, and suggestions on ways to address real and perceived shortcomings of available SPRA methods are presented and discussed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PRREACH: Probabilistic Risk Assessment Using Reachability for UAV Control
We present a new approach for designing risk-bounded controllers for Uncrewed Aerial Vehicles (UAVs). Existing frameworks for assessing risk of UAV operations rely on knowing the conditional probability of an incident oc…
A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment
Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-do…
BIG-bench Machine LearningRegional climate risk assessment from climate models using probabilistic machine learning
Accurate, actionable climate information at km scales is crucial for robust natural hazard risk assessment and infrastructure planning. Simulating climate at these resolutions remains intractable, forcing reliance on dow…
SpecificitySuper-ResolutionFast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning
This paper presents a graph-structured Gaussian process (GP) model for data-driven risk assessment of critical voltage constraints. The proposed GP is based on a novel kernel, named the vertex-degree kernel (VDK), that d…
Active LearningGaussian ProcessesUncertainty QuantificationAdvanced financial market forecasting: integrating Monte Carlo simulations with ensemble Machine Learning models
This paper presents a novel integration of Machine Learning (ML) models with Monte Carlo simulations to enhance financial forecasting and risk assessments in dynamic market environments. Traditional financial forecasting…