Predictive Capability Maturity Quantification using Bayesian Network

• URL: http://arxiv.org/abs/2010.03373v1
• Date: Mon, 31 Aug 2020 17:09:09 GMT
• Title: Predictive Capability Maturity Quantification using Bayesian Network
• Authors: Linyu Lin, Nam Dinh
• Abstract summary: In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Due to data gaps, validation becomes a decision-making process under uncertainties. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation process is needed to assess simulation adequacy, i.e., the degree to which M&S tools can adequately represent the system quantities of interest. However, due to data gaps, validation becomes a decision-making process under uncertainties. Expert knowledge and judgments are required to collect, choose, characterize, and integrate evidence toward the final adequacy decision. However, in validation frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249) and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such a decision-making process is largely implicit and obscure. When scenarios are complex, knowledge biases and unreliable judgments can be overlooked, which could increase uncertainty in the simulation adequacy result and the corresponding risks. Therefore, a framework is required to formalize the decision-making process for simulation adequacy in a practical, transparent, and consistent manner. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy based on information collected from validation activities. A case study is prepared for evaluating the adequacy of a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic forces onto static structures during an external flooding scenario. Comparing to the qualitative and implicit adequacy assessment, PCMQBN is able to improve confidence in the simulation adequacy result and to reduce expected loss in the risk-informed safety analysis.

Related papers

• Coupled Requirements-driven Testing of CPS: From Simulation To Reality [5.7736484832934325]
  Failures in safety-critical Cyber-Physical Systems (CPS) can lead to severe incidents impacting physical infrastructure or even harming humans. Current simulation and field testing practices, particularly in the domain of small Unmanned Aerial Systems (sUAS), are ad-hoc and lack a thorough, structured testing process. We have developed an initial framework for validating CPS, specifically focusing on sUAS and robotic applications.
  arXiv  Detail & Related papers  (2024-03-24T20:32:12Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations [8.8690305802668]
  A critical attribute of cyber-physical systems (CPS) is robustness, denoting its capacity to operate safely. This paper proposes a novel specification-based robustness, which characterizes the effectiveness of a controller in meeting a specified system requirement. We present an innovative two-layer simulation-based analysis framework designed to identify subtle robustness violations.
  arXiv  Detail & Related papers  (2023-11-13T16:44:43Z)
• Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry [0.0]
  Uncertainty estimates of simulation results are critical to the decision-making process. This paper develops a protocol to assess uncertainties in predictions of machine learning and mechanistic simulation models. It applies the protocol to test cases relevant to the gas distribution industry and presents learnings from its application.
  arXiv  Detail & Related papers  (2023-08-05T18:54:59Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)
• Structural Estimation of Partially Observable Markov Decision Processes [3.1614382994158956]
  We consider the structural estimation of the primitives of a POMDP model based upon the observable history of the process. We illustrate the estimation methodology with an application to optimal equipment replacement.
  arXiv  Detail & Related papers  (2020-08-02T15:04:27Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)
• Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles [6.198523595657983]
  The widescale deployment of Autonomous Vehicles seems to be imminent despite many safety challenges that are yet to be resolved. Existing standards focus on deterministic processes where the validation requires only a set of test cases that cover the requirements. This paper presents a new approach to compute the statistical characteristics of a system's behaviour by biasing automatically generated test cases towards the worst case scenarios.
  arXiv  Detail & Related papers  (2020-03-04T04:35:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.