Jon C. Helton

What is he best known for?

The fields of study he is best known for:

• Statistics
• Regression analysis
• Mechanical engineering

Jon C. Helton mostly deals with Uncertainty analysis, Uncertainty quantification, Sensitivity analysis, Statistics and Latin hypercube sampling. His research in Uncertainty analysis intersects with topics in Sampling, Regression analysis, Artificial intelligence and Sensitivity. His Sampling research incorporates themes from Variance decomposition of forecast errors, Sobol sequence, Stratified sampling and Space.

He focuses mostly in the field of Sensitivity, narrowing it down to topics relating to Data mining and, in certain cases, Distribution and Ranking. Jon C. Helton has included themes like Representation, Probability theory, Econometrics and Management science in his Uncertainty quantification study. His study looks at the relationship between Latin hypercube sampling and topics such as Stability, which overlap with Sample size determination, Hypercube, Kruskal–Wallis one-way analysis of variance, Statistic and Type I and type II errors.

His most cited work include:

• Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (1424 citations)
• Survey of sampling-based methods for uncertainty and sensitivity analysis (886 citations)
• Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal (573 citations)

What are the main themes of his work throughout his whole career to date?

Radioactive waste, Uncertainty quantification, Uncertainty analysis, Waste Isolation Pilot Plant and Waste management are his primary areas of study. Jon C. Helton interconnects Waste disposal and Civil engineering in the investigation of issues within Radioactive waste. His study looks at the relationship between Uncertainty quantification and fields such as Representation, as well as how they intersect with chemical problems.

Jon C. Helton has researched Uncertainty analysis in several fields, including Latin hypercube sampling, Mathematical optimization and Operations research. His work carried out in the field of Latin hypercube sampling brings together such families of science as Sampling, Probabilistic risk assessment, Theoretical computer science and Sensitivity. The various areas that he examines in his Waste Isolation Pilot Plant study include Environmental engineering, Transuranic waste and Drilling.

He most often published in these fields:

• Radioactive waste (30.68%)
• Uncertainty quantification (27.27%)
• Uncertainty analysis (27.27%)

What were the highlights of his more recent work (between 2011-2021)?

• High-level waste (12.50%)
• Uncertainty quantification (27.27%)
• Uncertainty analysis (27.27%)

In recent papers he was focusing on the following fields of study:

Jon C. Helton focuses on High-level waste, Uncertainty quantification, Uncertainty analysis, Radioactive waste and Reliability engineering. Jon C. Helton combines subjects such as Latin hypercube sampling and Sensitivity with his study of Uncertainty quantification. His Latin hypercube sampling study frequently links to related topics such as Operations research.

The concepts of his Sensitivity study are interwoven with issues in Sampling and Statistics. By researching both Uncertainty analysis and Metamodeling, Jon C. Helton produces research that crosses academic boundaries. His studies in Radioactive waste integrate themes in fields like System safety and Spent nuclear fuel.

Between 2011 and 2021, his most popular works were:

• Uncertainty and sensitivity analysis in performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada (29 citations)
• Use of replicated Latin hypercube sampling to estimate sampling variance in uncertainty and sensitivity analysis results for the geologic disposal of radioactive waste (29 citations)
• Analysis of computationally demanding models with continuous and categorical inputs (27 citations)

In his most recent research, the most cited papers focused on:

• Statistics
• Mathematical analysis
• Mechanical engineering

His scientific interests lie mostly in Uncertainty analysis, High-level waste, Radioactive waste, Uncertainty quantification and Civil engineering. His Uncertainty analysis research includes elements of Reliability engineering, Machine learning, Categorical variable, Surrogate model and Computational model. His studies link System safety with Radioactive waste.

His work deals with themes such as Latin hypercube sampling and Sensitivity, which intersect with Uncertainty quantification. His research on Latin hypercube sampling concerns the broader Statistics. The Sensitivity study combines topics in areas such as Econometrics, Sample size determination and Visual inspection.