What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Nonlinear system, Applied mathematics, Control theory, Chaotic and Observability.
His Nonlinear system research incorporates themes from Embedding, Estimation theory, Statistical physics, Mathematical optimization and Function.
Luis A. Aguirre combines subjects such as Constraint, Extended Kalman filter and Unscented transform with his study of Mathematical optimization.
His Applied mathematics research incorporates elements of Discrete mathematics, Fixed point, Meteorology, Benchmark and Series.
Luis A. Aguirre has researched Control theory in several fields, including Polynomial, Stability and Identification.
His work on Lyapunov exponent as part of general Chaotic research is often related to Biological system, Tumor cells and Inductor, thus linking different fields of science.
His most cited work include:
- Improved structure selection for nonlinear models based on term clustering (103 citations)
- Modeling Nonlinear Dynamics and Chaos: A Review (82 citations)
- Relation between observability and differential embeddings for nonlinear dynamics. (79 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Nonlinear system, Applied mathematics, Observability and Polynomial.
His work on Kalman filter as part of general Control theory research is frequently linked to Context, bridging the gap between disciplines.
The concepts of his Nonlinear system study are interwoven with issues in Nonlinear system identification, Identification, Attractor, Mathematical optimization and Series.
His research investigates the link between Mathematical optimization and topics such as Estimation theory that cross with problems in Function.
His Applied mathematics study combines topics in areas such as Discrete mathematics and Bifurcation.
His Polynomial research is multidisciplinary, relying on both Structure, Algorithm and Nonlinear autoregressive exogenous model.
He most often published in these fields:
- Control theory (41.46%)
- Nonlinear system (40.00%)
- Applied mathematics (21.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Nonlinear system (40.00%)
- Control theory (41.46%)
- Observability (14.15%)
In recent papers he was focusing on the following fields of study:
Luis A. Aguirre mainly focuses on Nonlinear system, Control theory, Observability, Topology and Autoregressive model.
His Nonlinear system research is multidisciplinary, incorporating perspectives in Dynamical systems theory, Nonlinear system identification, System identification, Applied mathematics and Mathematical model.
His Control theory study incorporates themes from Estimation theory, Fixed point and Identification.
His Observability study combines topics from a wide range of disciplines, such as Algorithm and Scalar.
The Measure research Luis A. Aguirre does as part of his general Topology study is frequently linked to other disciplines of science, such as Observable, therefore creating a link between diverse domains of science.
His work deals with themes such as Polynomial and Compensation, which intersect with Autoregressive model.
Between 2017 and 2021, his most popular works were:
- Structural, dynamical and symbolic observability: From dynamical systems to networks. (19 citations)
- Nonlinear graph-based theory for dynamical network observability. (13 citations)
- Control and Observability Aspects of Phase Synchronization (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Luis A. Aguirre focuses on Nonlinear system, System identification, Topology, Nonlinear system identification and Observability.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Initial value problem, Lipschitz continuity, Optimization problem and Applied mathematics.
His System identification research is multidisciplinary, incorporating elements of Nonlinear autoregressive exogenous model, Mathematical model, Human–computer interaction and Sample.
His study on Dimension, Function and Vector field is often connected to Observable as part of broader study in Topology.
His Nonlinear system identification study integrates concerns from other disciplines, such as Estimation theory, Probabilistic logic, Selection and Linear algebra.
His biological study spans a wide range of topics, including State variable, Network dynamics and Jacobian matrix and determinant.
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