A5080764543- Probabilistic and Robust Engineering Design
- Advanced Multi-Objective Optimization Algorithms
- Nuclear reactor physics and engineering
- Gaussian Processes and Bayesian Inference
- Optimal Experimental Design Methods
- Advanced Data Processing Techniques
- High-Velocity Impact and Material Behavior
- Distributed and Parallel Computing Systems
- Nuclear Physics and Applications
- Model Reduction and Neural Networks
- Computational Fluid Dynamics and Aerodynamics
- Simulation Techniques and Applications
- Scientific Computing and Data Management
- Data Analysis with R
- Bayesian Methods and Mixture Models
- Magnetic confinement fusion research
- Advanced Statistical Methods and Models
- Nuclear Materials and Properties
- Modular Robots and Swarm Intelligence
- Advanced Data Storage Technologies
- Reliability and Maintenance Optimization
- Statistical Methods and Inference
- Computer Graphics and Visualization Techniques
- Granular flow and fluidized beds
- Statistical Mechanics and Entropy
Lawrence Livermore National Laboratory
2011-2023
Arizona State University
2009-2010
Scientific workflows have been used almost universally across scientific domains, and underpinned some of the most significant discoveries past several decades. Many these high computational, storage, and/or communication demands, thus must execute on a wide range large-scale platforms, from large clouds to upcoming exascale high-performance computing (HPC) platforms. These executions be managed using software infrastructure. Due popularity workflows, workflow management systems (WMSs)...
Understanding and describing expensive black box functions such as physical simulations is a common problem in many application areas. One example the recent interest uncertainty quantification with goal of discovering relationship between potentially large number input parameters output simulation. Typically, simulation to evaluate thus sampling parameter space necessarily small. As result choosing "good" set samples at which crucial glean much information possible from fewest samples....
Through sensitivity analysis, we study how variability in the output of a strength model can be apportioned to different sources uncertainty input. Determining these relationships has become first step use models that precedes their calibration experimental data. Typical analysis techniques are designed for with scalar—rather than functional—output. To quantify parameter sensitivities models, extend global indices functional outputs. In particular, BASS R package, which employs Bayesian...
Predicting flight-performance of a hypersonic flight vehicle requires characterization the complex aerothermodynamic phenomena present across envelope. A stable and accurate trajectory simulation an aerodynamic database that covers wide variety conditions (aka using statistical terminology, large design space). When preparing such database, computational cost becomes critical to conserve limited resources while ensuring quantities interest are well resolved. In this study, computationally...
Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 15 July 2020Accepted: 14 October 2021Published online: 11 January 2022Keywordsemulator, functional data analysis, registration, warping, computer experiment, monotonicityAMS Subject Headings62P35, 62R10, 62J02Publication DataISSN (online): 2166-2525Publisher: Society for Industrial and Applied MathematicsCODEN: sjuqa3
Abstract In regimes of high strain rate, the strength materials often cannot be measured directly in experiments. Instead, is inferred based on an experimental observable, such as a change shape, that matched by simulations supported known model. hole closure experiments, rate and degree to which central plate material closes during dynamic loading event are used infer parameters. Due complexity experiment, many computationally expensive, three‐dimensional necessary train emulator for...