What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mechanical engineering
Reliability, Mathematical optimization, Algorithm, Data mining and Reliability engineering are his primary areas of study.
His Reliability study combines topics from a wide range of disciplines, such as Random variable, Kriging, Computational model, Bayesian network and Monte Carlo method.
Sankaran Mahadevan combines subjects such as Uncertainty quantification, Limit, Limit state design and Engineering design process with his study of Mathematical optimization.
His work carried out in the field of Algorithm brings together such families of science as Statistical hypothesis testing, Uncertainty analysis, Bayesian statistics and Probability distribution.
His study in Data mining is interdisciplinary in nature, drawing from both Centrality, Node, Analytic hierarchy process, Process and Complex network.
He combines subjects such as Probabilistic logic, Probabilistic-based design optimization and Nonlinear system with his study of Reliability engineering.
His most cited work include:
- Reliability Assessment Using Stochastic Finite Element Analysis (508 citations)
- Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (457 citations)
- Supplier selection using AHP methodology extended by D numbers (305 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Reliability, Mathematical optimization, Uncertainty quantification, Reliability engineering and Data mining.
His research investigates the connection with Reliability and areas like Structural engineering which intersect with concerns in Stress.
The study incorporates disciplines such as Sensitivity, Limit, Limit state design and Nonlinear system in addition to Mathematical optimization.
His study in Uncertainty quantification is interdisciplinary in nature, drawing from both Sensitivity analysis, Uncertainty analysis, Probability distribution, Bayesian network and Surrogate model.
Sankaran Mahadevan performs integrative study on Reliability engineering and Component in his works.
As part of the same scientific family, he usually focuses on Data mining, concentrating on Bayesian statistics and intersecting with Statistical hypothesis testing.
He most often published in these fields:
- Reliability (25.26%)
- Mathematical optimization (21.16%)
- Uncertainty quantification (19.45%)
What were the highlights of his more recent work (between 2016-2021)?
- Uncertainty quantification (19.45%)
- Mathematical optimization (21.16%)
- Data mining (15.19%)
In recent papers he was focusing on the following fields of study:
His main research concerns Uncertainty quantification, Mathematical optimization, Data mining, Reliability engineering and Bayesian network.
His work carried out in the field of Uncertainty quantification brings together such families of science as Subspace topology, Dimensionality reduction, Sensitivity analysis, Data analysis and Engineering design process.
His Mathematical optimization research focuses on Sensitivity and how it relates to Polynomial chaos and Computation.
His studies in Data mining integrate themes in fields like Monte Carlo method and Process.
His studies deal with areas such as Reliability and Probabilistic logic as well as Reliability engineering.
His Bayesian network research includes elements of Mixture model, Algorithm and Bayesian inference.
Between 2016 and 2021, his most popular works were:
- Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin (83 citations)
- Dependence assessment in human reliability analysis based on D numbers and AHP (70 citations)
- Reliability analysis with linguistic data: An evidential network approach (69 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mechanical engineering
His primary scientific interests are in Data mining, Mathematical optimization, Reliability engineering, Uncertainty quantification and Reliability.
His Data mining study integrates concerns from other disciplines, such as Artificial neural network, Dynamic Bayesian network, Monte Carlo method and Bayesian inference.
The Mathematical optimization study combines topics in areas such as Sampling and Path.
His Reliability engineering research integrates issues from Predictive modelling and Probabilistic logic.
His Uncertainty quantification study combines topics in areas such as Reliability, Data-driven, Joint probability distribution, Process and Engineering design process.
His Reliability study incorporates themes from Stochastic process and Survival analysis.
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