What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mechanical engineering
His main research concerns Fuzzy logic, Mathematical optimization, Reliability, Data mining and Probabilistic logic.
In his study, which falls under the umbrella issue of Fuzzy logic, Randomness, Interval and Uncertainty quantification is strongly linked to Imprecise probability.
His Mathematical optimization research includes elements of Algorithm, Finite element method and Probability distribution.
The Reliability study combines topics in areas such as Upper and lower bounds and Operations research.
His Data mining study integrates concerns from other disciplines, such as Fuzzy set operations, Fuzzy classification and Fuzzy measure theory.
His work on Probabilistic method is typically connected to Sea state as part of general Probabilistic logic study, connecting several disciplines of science.
His most cited work include:
- Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics (315 citations)
- Imprecise probabilities in engineering analyses (232 citations)
- Fuzzy structural analysis using α-level optimization (205 citations)
What are the main themes of his work throughout his whole career to date?
Michael Beer focuses on Reliability, Mathematical optimization, Uncertainty quantification, Algorithm and Applied mathematics.
His biological study spans a wide range of topics, including Complex system, Reliability engineering and Structural engineering.
His Mathematical optimization research incorporates themes from Stochastic process, Optimal design, Random variable and Fuzzy logic.
The concepts of his Uncertainty quantification study are interwoven with issues in Bayesian inference, Imprecise probability, Probabilistic logic, Interval and Monte Carlo method.
Michael Beer has researched Algorithm in several fields, including Bhattacharyya distance, Statistical model, Kriging and Sensitivity.
His Applied mathematics research focuses on Nonlinear system and how it connects with Response spectrum.
He most often published in these fields:
- Reliability (45.06%)
- Mathematical optimization (46.27%)
- Uncertainty quantification (47.23%)
What were the highlights of his more recent work (between 2019-2021)?
- Uncertainty quantification (47.23%)
- Reliability (45.06%)
- Random variable (31.33%)
In recent papers he was focusing on the following fields of study:
Michael Beer mainly investigates Uncertainty quantification, Reliability, Random variable, Mathematical optimization and Applied mathematics.
Michael Beer has included themes like Finite element method, Bayesian inference, Imprecise probability, Probabilistic logic and Realization in his Uncertainty quantification study.
As a member of one scientific family, Michael Beer mostly works in the field of Reliability, focusing on Reliability engineering and, on occasion, Mechanism and Measure.
The study incorporates disciplines such as Stochastic process, Probability density function and Class in addition to Random variable.
Michael Beer incorporates Mathematical optimization and Context in his research.
His Applied mathematics research includes themes of Maxima and minima, Operator norm, Polynomial chaos, Upper and lower bounds and Propagation of uncertainty.
Between 2019 and 2021, his most popular works were:
- Reliability and importance analysis of uncertain system with common cause failures based on survival signature (16 citations)
- Reliability and importance analysis of uncertain system with common cause failures based on survival signature (16 citations)
- Reliability and importance analysis of uncertain system with common cause failures based on survival signature (16 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mechanical engineering
Uncertainty quantification, Algorithm, Bayesian inference, Stochastic simulation and Imprecise probability are his primary areas of study.
His Uncertainty quantification research integrates issues from Operator norm, Importance sampling, Applied mathematics and Mechanism.
His Algorithm research is multidisciplinary, relying on both Failure mode and effects analysis, Bayesian network and Plot.
His study in Bayesian inference is interdisciplinary in nature, drawing from both Complex system, Probabilistic logic, Reliability engineering and Measure.
His Stochastic simulation study incorporates themes from Estimator and Rare events.
His research investigates the connection with Estimator and areas like Sensitivity which intersect with concerns in Probability density function, Random variable, Mathematical optimization and Probability measure.
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