What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Mathematical analysis
- Composite material
His primary scientific interests are in Finite element method, Plasticity, Mechanics, Dislocation and Classical mechanics.
His Finite element method research entails a greater understanding of Structural engineering.
The concepts of his Plasticity study are interwoven with issues in Finite strain theory, Deformation, Constitutive equation, Peierls stress and Forensic engineering.
His Mechanics research incorporates elements of Singular perturbation, Grain boundary, Deformation, Fracture toughness and Energy minimization.
His Dislocation research incorporates themes from Slip and Nanoindentation.
His Classical mechanics research integrates issues from Calculus of variations, Boundary value problem, Tangent and Applied mathematics.
His most cited work include:
- Computational modelling of impact damage in brittle materials (1536 citations)
- Quasicontinuum analysis of defects in solids (1261 citations)
- FINITE-DEFORMATION IRREVERSIBLE COHESIVE ELEMENTS FOR THREE-DIMENSIONAL CRACK-PROPAGATION ANALYSIS (1066 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Finite element method, Mechanics, Mathematical analysis, Composite material and Plasticity.
His Finite element method study integrates concerns from other disciplines, such as Discretization, Geometry, Fracture mechanics and Classical mechanics.
A large part of his Fracture mechanics studies is devoted to Cohesive zone model.
His studies in Classical mechanics integrate themes in fields like Boundary value problem and Dislocation.
His study involves Brittleness and Stress, a branch of Composite material.
His study in Plasticity is interdisciplinary in nature, drawing from both Hardening, Viscoplasticity, Constitutive equation and Finite strain theory.
He most often published in these fields:
- Finite element method (21.80%)
- Mechanics (15.00%)
- Mathematical analysis (14.60%)
What were the highlights of his more recent work (between 2017-2021)?
- Plasticity (13.80%)
- Mathematical analysis (14.60%)
- Data-driven (5.40%)
In recent papers he was focusing on the following fields of study:
Michael Ortiz mainly investigates Plasticity, Mathematical analysis, Data-driven, Elasticity and Material data.
The Plasticity study combines topics in areas such as Fractional calculus, Envelope and Simple shear.
His study focuses on the intersection of Mathematical analysis and fields such as Minification with connections in the field of Data point.
His biological study spans a wide range of topics, including Phase space and Computational mechanics.
His Applied mathematics study combines topics from a wide range of disciplines, such as Point, Boundary and Fracture mechanics.
His Algorithm study incorporates themes from Boundary value problem and Constitutive equation.
Between 2017 and 2021, his most popular works were:
- Data-driven computing in dynamics (67 citations)
- Model-Free Data-Driven inelasticity (55 citations)
- Model-Free Data-Driven inelasticity (55 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Mathematical analysis
- Composite material
Data-driven, Elasticity, Material data, Plasticity and Phase space are his primary areas of study.
His Data-driven study combines topics in areas such as Weighting, Tangent space, Mathematical optimization and Algorithm.
His work carried out in the field of Algorithm brings together such families of science as Solid mechanics, Displacement, State space and Boundary value problem.
His research in Plasticity intersects with topics in Viscoplasticity, Model free, Statistical physics, Stress–strain curve and Viscoelasticity.
Within one scientific family, he focuses on topics pertaining to Subspace topology under Linear elasticity, and may sometimes address concerns connected to Applied mathematics.
His Applied mathematics research includes elements of Energy functional, Fracture mechanics, Noise, Point and Data set.
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