What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Artificial intelligence
- Composite material
The scientist’s investigation covers issues in Curse of dimensionality, Algorithm, Applied mathematics, Discretization and Mathematical optimization.
His work deals with themes such as Theoretical computer science and Process, which intersect with Curse of dimensionality.
He combines subjects such as Finite element method and Nonlinear system with his study of Algorithm.
His work carried out in the field of Applied mathematics brings together such families of science as Tangent, Tangent stiffness matrix, Partial differential equation, Boundary value problem and Element.
His research integrates issues of Space, Finite difference, Representation and Basis in his study of Discretization.
His Mathematical optimization research is multidisciplinary, incorporating elements of Work, Homogenization, Function, Mobile device and Computational mechanics.
His most cited work include:
- A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids (424 citations)
- A Short Review on Model Order Reduction Based on Proper Generalized Decomposition (395 citations)
- Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models (248 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Composite material, Applied mathematics, Model order reduction and Mechanics.
His Mathematical optimization study also includes fields such as
- Algorithm most often made with reference to Curse of dimensionality,
- Representation which connect with Discretization.
He combines topics linked to Finite element method with his work on Applied mathematics.
The Finite element method study combines topics in areas such as Mechanical engineering and Computer simulation.
His research ties Real-time simulation and Model order reduction together.
The study incorporates disciplines such as Kinematics and Classical mechanics in addition to Mechanics.
He most often published in these fields:
- Mathematical optimization (15.48%)
- Composite material (13.39%)
- Applied mathematics (12.87%)
What were the highlights of his more recent work (between 2018-2021)?
- Model order reduction (12.70%)
- Algorithm (9.57%)
- Representation (9.22%)
In recent papers he was focusing on the following fields of study:
Francisco Chinesta mainly focuses on Model order reduction, Algorithm, Representation, Applied mathematics and Computational intelligence.
His Model order reduction research incorporates elements of Finite element method, Parametrization, Mathematical optimization, Electric motor and Nonlinear system.
Francisco Chinesta has researched Mathematical optimization in several fields, including Subspace topology and Limit.
His research investigates the link between Algorithm and topics such as Curse of dimensionality that cross with problems in Latent variable, Artificial neural network and Nonlinear dimensionality reduction.
Representation and Parametric model are two areas of study in which Francisco Chinesta engages in interdisciplinary work.
His Applied mathematics study combines topics from a wide range of disciplines, such as Basis, Dimension, Affine transformation, Frequency domain and Discretization.
Between 2018 and 2021, his most popular works were:
- Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data (44 citations)
- Thermodynamically consistent data-driven computational mechanics (39 citations)
- Hybrid constitutive modeling: data-driven learning of corrections to plasticity models (38 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Artificial intelligence
- Composite material
His primary scientific interests are in Model order reduction, Algorithm, Applied mathematics, Affine transformation and Representation.
His Model order reduction study combines topics in areas such as Fault, Nonlinear dimensionality reduction, Reduction and Hardware-in-the-loop simulation.
His research in Algorithm intersects with topics in Limit, Finite element method, Statistical shape analysis and Rank.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Frequency domain and Nonlinear system.
His study looks at the relationship between Affine transformation and topics such as Collocation, which overlap with Discretization, Separable space and Projection.
Francisco Chinesta works on Mathematical optimization which deals in particular with Proper generalized decomposition.
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