A5014772207- Nuclear reactor physics and engineering
- Probabilistic and Robust Engineering Design
- Nuclear Materials and Properties
- Nuclear Engineering Thermal-Hydraulics
- Fault Detection and Control Systems
- Software Reliability and Analysis Research
- Reliability and Maintenance Optimization
- Statistical Distribution Estimation and Applications
- Parallel Computing and Optimization Techniques
- Simulation Techniques and Applications
- Scientific Measurement and Uncertainty Evaluation
- Graphite, nuclear technology, radiation studies
- Markov Chains and Monte Carlo Methods
- Reservoir Engineering and Simulation Methods
- Software Engineering Techniques and Practices
- Software Engineering Research
- Nuclear and radioactivity studies
- Risk and Safety Analysis
Johns Hopkins University
2022-2023
ORCID
2023
This paper presents the latest improvements introduced in Version 4 of UQpy, Uncertainty Quantification with Python, library. In version, code was restructured to conform Python coding conventions, refactored simplify previous tightly coupled features, and improve its extensibility modularity. To robustness software engineering best practices were adopted. A new development workflow significantly improved collaboration between team members, continuous integration automated testing ensured...
Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when small. Exploiting low-fidelity can make this process more feasible, but merging information from multiple and poses several challenges. This paper presents a robust multifidelity surrogate modeling strategy in which assembled an active-learning on-the-fly model adequacy assessment set within subset simulation framework efficient...
Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes estimation failure probabilities significant computational challenge for complex real-world problems, particularly when is rare event. To implement multifidelity modeling, estimators efficiently combine information from multiple models/sources are necessary. In past works, variance reduction techniques Control Variates (CV) and Importance Sampling (IS)...
In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive evaluate associated density function. For such cases, MCMC has proven an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of...
This paper presents the latest improvements introduced in Version 4 of UQpy, Uncertainty Quantification with Python, library. In version, code was restructured to conform Python coding conventions, refactored simplify previous tightly coupled features, and improve its extensibility modularity. To robustness software engineering best practices were adopted. A new development workflow significantly improved collaboration between team members, continous integration automated testing ensured...
The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear proposed to be used for multiple modern technologies. Therefore, characterizing its safety vital the reliable operation of However, TRISO failure probabilities are small and computational model time consuming evaluate them using traditional Monte Carlo-type approaches. In paper, we present multifidelity active learning approach efficiently estimate given an expensive model. Active suggests next best training set...
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear and determining its reliability critical for the success of advanced technologies. However, TRISO failure probabilities are small associated computational models expensive. We used coupled active learning, multifidelity modeling, subset simulation to estimate fuels using several 1D 2D models. With we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) For models,...
Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when small. Exploiting low-fidelity can make this process more feasible, but merging information from multiple and poses several challenges. This paper presents a robust multi-fidelity surrogate modeling strategy in which assembled an active learning on-the-fly model adequacy assessment set within subset simulation framework efficient...