What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Control theory
His main research concerns Crystallization, Control theory, Optimal control, Analytical chemistry and Nucleation.
His Crystallization research integrates issues from Supersaturation, Feedback control, Biological system, Crystal and Process engineering.
His studies deal with areas such as Process control, Quadratic programming, Parametric statistics and Model predictive control as well as Control theory.
Richard D. Braatz combines subjects such as Control, Robustness and Nonlinear system with his study of Optimal control.
His Nucleation research includes themes of Mechanics, Computational fluid dynamics and Micromixing.
His Mathematical optimization study integrates concerns from other disciplines, such as Probability distribution, Open-loop controller and Polynomial chaos.
His most cited work include:
- Fault Detection and Diagnosis in Industrial Systems (1291 citations)
- Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis (488 citations)
- A tutorial on linear and bilinear matrix inequalities (463 citations)
What are the main themes of his work throughout his whole career to date?
Richard D. Braatz mostly deals with Control theory, Mathematical optimization, Crystallization, Model predictive control and Optimal control.
His study in Process control extends to Control theory with its themes.
His research investigates the link between Mathematical optimization and topics such as Polynomial chaos that cross with problems in Applied mathematics.
His Crystallization research includes themes of Nucleation, Supersaturation, Mechanics, Crystal and Process engineering.
Many of his studies on Optimal control apply to Distributed parameter system as well.
His study on Robust control is covered under Control engineering.
He most often published in these fields:
- Control theory (24.71%)
- Mathematical optimization (14.86%)
- Crystallization (14.52%)
What were the highlights of his more recent work (between 2016-2021)?
- Model predictive control (9.02%)
- Crystallization (14.52%)
- Control theory (24.71%)
In recent papers he was focusing on the following fields of study:
Model predictive control, Crystallization, Control theory, Applied mathematics and Polynomial chaos are his primary areas of study.
His Model predictive control research is multidisciplinary, relying on both Control engineering and Battery.
His work carried out in the field of Crystallization brings together such families of science as Nucleation, Suspension, Mixing, Mechanics and Crystal.
His Control theory research is multidisciplinary, incorporating perspectives in Control and Voltage.
Within one scientific family, Richard D. Braatz focuses on topics pertaining to Nonlinear system under Applied mathematics, and may sometimes address concerns connected to Statistical physics.
His Polynomial chaos study also includes
- Probabilistic logic which intersects with area such as Parametric statistics, Dynamical systems theory, Fault detection and isolation, Robustness and Observer,
- Linear system that connect with fields like Stochastic process and Mathematical optimization.
Between 2016 and 2021, his most popular works were:
- Data-driven prediction of battery cycle life before capacity degradation (223 citations)
- Closed-loop optimization of fast-charging protocols for batteries with machine learning. (77 citations)
- Stochastic model predictive control with joint chance constraints (42 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Electrical engineering
The scientist’s investigation covers issues in Crystallization, Control theory, Artificial intelligence, Model predictive control and Data mining.
His Crystallization research integrates issues from Suspension, Mixing, Bayesian probability, Mechanics and Crystal.
His Control theory study combines topics in areas such as Parametric statistics, Lithium-ion battery, Polynomial chaos, Control and Data stream mining.
His study explores the link between Control and topics such as Robustness that cross with problems in Probabilistic logic.
His studies deal with areas such as Fault, Fault detection and isolation, Machine learning and Pattern recognition as well as Artificial intelligence.
His Model predictive control study combines topics from a wide range of disciplines, such as Joint, Time complexity, State, Piecewise affine and Battery management systems.
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