What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Mathematical analysis
Alexander N. Gorban focuses on Mathematical analysis, Statistical physics, Nonlinear system, Invariant and Invariant manifold.
The concepts of his Mathematical analysis study are interwoven with issues in Dynamical systems theory, Shaping and Boltzmann equation.
His research in Statistical physics intersects with topics in Markov renewal process and Entropy.
His biological study spans a wide range of topics, including Basis, Kinetic energy and Applied mathematics.
His Invariant research incorporates elements of Non-equilibrium thermodynamics, Newton's method, Taylor series and Ansatz.
The various areas that Alexander N. Gorban examines in his Invariant manifold study include Grid, Numerical analysis and Dissipative system.
His most cited work include:
- Principal Manifolds for Data Visualization and Dimension Reduction (207 citations)
- Method of invariant manifold for chemical kinetics (205 citations)
- Invariant Manifolds for Physical and Chemical Kinetics (196 citations)
What are the main themes of his work throughout his whole career to date?
Alexander N. Gorban spends much of his time researching Statistical physics, Artificial intelligence, Mathematical analysis, Nonlinear system and Artificial neural network.
His Statistical physics research is multidisciplinary, incorporating perspectives in Boltzmann constant, Lattice Boltzmann methods, Limit and Boltzmann equation.
His Lattice Boltzmann methods study frequently links to adjacent areas such as HPP model.
His work deals with themes such as Natural language processing, Machine learning and Pattern recognition, which intersect with Artificial intelligence.
Alexander N. Gorban studies Invariant manifold, a branch of Mathematical analysis.
His research in Nonlinear system tackles topics such as Applied mathematics which are related to areas like Estimation theory.
He most often published in these fields:
- Statistical physics (18.21%)
- Artificial intelligence (13.19%)
- Mathematical analysis (11.35%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (13.19%)
- Curse of dimensionality (6.33%)
- Artificial neural network (8.71%)
In recent papers he was focusing on the following fields of study:
Alexander N. Gorban mainly focuses on Artificial intelligence, Curse of dimensionality, Artificial neural network, Deep learning and Theoretical computer science.
His Artificial intelligence research is multidisciplinary, incorporating elements of Simple, Natural language processing, Machine learning and Pattern recognition.
His research on Curse of dimensionality also deals with topics like
- Simple and High dimensional most often made with reference to Simplicity,
- Concentration of measure that connect with fields like Discriminant, Statistical mechanics, Linear separability and Lipschitz continuity.
His Statistical mechanics research entails a greater understanding of Statistical physics.
His Statistical physics research incorporates themes from Limit and Kinesis.
Alexander N. Gorban combines subjects such as Network model and Neuron with his study of Artificial neural network.
Between 2017 and 2021, his most popular works were:
- Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM (78 citations)
- Blessing of dimensionality: mathematical foundations of the statistical physics of data. (60 citations)
- Correction of AI systems by linear discriminants: Probabilistic foundations (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Mathematical analysis
His main research concerns Artificial intelligence, Simple, Artificial neural network, Theoretical computer science and High dimensional.
He has researched Artificial intelligence in several fields, including Development, Identification and Pattern recognition.
His Simple research is multidisciplinary, relying on both Structure, Measure and Perceptron.
His Measure study combines topics from a wide range of disciplines, such as Linear separability, Concentration of measure, Extreme point, Statistical physics and Lipschitz continuity.
His work carried out in the field of Artificial neural network brings together such families of science as Conceptual framework, Dimension, Multi-agent system, Deep learning and Convolutional neural network.
His work blends Theoretical computer science and Principal studies together.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
