What is he best known for?
The fields of study he is best known for:
- Composite material
- Thermodynamics
- Artificial intelligence
Michel Grédiac spends much of his time researching Inverse problem, Finite element method, Constitutive equation, Optics and Displacement.
Michel Grédiac combines subjects such as Algorithm, Stress, Digital image correlation and Virtual work with his study of Inverse problem.
His Finite element method study integrates concerns from other disciplines, such as Numerical analysis, Mathematical analysis, Geometry, Tensile testing and Composite material.
His Composite material research is multidisciplinary, relying on both Transverse plane and Structural engineering.
The Constitutive equation study combines topics in areas such as Mathematical optimization, Noise and System identification.
His Displacement study which covers Grid method multiplication that intersects with Image resolution.
His most cited work include:
- Overview of Identification Methods of Mechanical Parameters Based on Full-field Measurements (456 citations)
- The use of full-field measurement methods in composite material characterization: interest and limitations (208 citations)
- The Virtual Fields Method (184 citations)
What are the main themes of his work throughout his whole career to date?
Composite material, Grid method multiplication, Structural engineering, Displacement and Digital image correlation are his primary areas of study.
Michel Grédiac has included themes like Thermography and Thermoelastic damping in his Composite material study.
His research investigates the link between Grid method multiplication and topics such as Full field that cross with problems in Strain distribution.
His work deals with themes such as Shear and Mechanical engineering, which intersect with Structural engineering.
In his research, Image resolution, Noise, Inverse problem, Virtual work and Mathematical optimization is intimately related to Algorithm, which falls under the overarching field of Digital image correlation.
His Constitutive equation research includes themes of Identification and System identification.
He most often published in these fields:
- Composite material (40.60%)
- Grid method multiplication (25.64%)
- Structural engineering (25.21%)
What were the highlights of his more recent work (between 2016-2021)?
- Digital image correlation (20.94%)
- Displacement (20.94%)
- Composite material (40.60%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Digital image correlation, Displacement, Composite material, Grid method multiplication and Algorithm.
His biological study spans a wide range of topics, including Displacement field, Speckle pattern, Computer vision, Artificial intelligence and Metrology.
His study looks at the intersection of Displacement and topics like Spectral method with Power and Checkerboard.
His work carried out in the field of Composite material brings together such families of science as Mechanical dissipation and Degree.
The concepts of his Grid method multiplication study are interwoven with issues in Milicia excelsa, Cracking and Fracture.
His Algorithm research integrates issues from Image resolution, Kinematics, Translation and Rotation.
Between 2016 and 2021, his most popular works were:
- A Critical Comparison of Some Metrological Parameters Characterizing Local Digital Image Correlation and Grid Method (45 citations)
- Towards standardized mechanical characterization of microbial biofilms: analysis and critical review (43 citations)
- Rendering Deformed Speckle Images with a Boolean Model (18 citations)
In his most recent research, the most cited papers focused on:
- Composite material
- Thermodynamics
- Artificial intelligence
Michel Grédiac mainly investigates Digital image correlation, Context, Algorithm, Grid method multiplication and Displacement.
His research integrates issues of Finite element method, Joint, Thermal contraction, Speckle pattern and Bearing in his study of Digital image correlation.
The study incorporates disciplines such as Image resolution, Kinematics, Rotation, Translation and Deformation in addition to Algorithm.
His studies deal with areas such as Deconvolution, Image sensor, Engineering drawing, Feature and Grid as well as Image resolution.
His Grid method multiplication study incorporates themes from Creep, Humidity, Mechanics and Viscoelasticity.
His research in Displacement intersects with topics in Strain, Checkerboard, Surface, Fourier transform and Metrology.
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