What is he best known for?
The fields of study Manolis Papadrakakis is best known for:
- Finite element method
- Statistics
- Algorithm
Structural engineering combines with fields such as Buckling and Earthquake engineering in his work.
His Finite element method study typically links adjacent topics like Structural engineering.
He brings together Mathematical optimization and Applied mathematics to produce work in his papers.
In his research, Manolis Papadrakakis undertakes multidisciplinary study on Applied mathematics and Mathematical optimization.
Borrowing concepts from Algorithm, Manolis Papadrakakis weaves in ideas under Statistics.
In his papers, he integrates diverse fields, such as Algorithm and Statistics.
As part of his studies on Quantum mechanics, he often connects relevant subjects like Reliability (semiconductor).
Manolis Papadrakakis regularly links together related areas like Power (physics) in his Reliability (semiconductor) studies.
Power (physics) is often connected to Quantum mechanics in his work.
His most cited work include:
- Reliability-based structural optimization using neural networks and Monte Carlo simulation (407 citations)
- Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation (222 citations)
- Structural optimization using evolution strategies and neural networks (180 citations)
What are the main themes of his work throughout his whole career to date
His work on Quantum mechanics as part of his general Nonlinear system study is frequently connected to Structural engineering, thereby bridging the divide between different branches of science.
Many of his studies on Quantum mechanics apply to Nonlinear system and Eigenvalues and eigenvectors as well.
His work on Finite element method expands to the thematically related Structural engineering.
He performs multidisciplinary study in the fields of Finite element method and Domain decomposition methods via his papers.
He integrates Mathematical optimization and Optimization problem in his studies.
He undertakes multidisciplinary investigations into Optimization problem and Mathematical optimization in his work.
In his works, he undertakes multidisciplinary study on Applied mathematics and Mathematical analysis.
He integrates Mathematical analysis and Applied mathematics in his research.
His study in Conjugate gradient method extends to Algorithm with its themes.
Manolis Papadrakakis most often published in these fields:
- Structural engineering (64.43%)
- Mathematical optimization (47.65%)
- Finite element method (45.64%)
What were the highlights of his more recent work (between 2017-2022)?
- Structural engineering (100.00%)
- Quantum mechanics (80.00%)
- Finite element method (80.00%)
In recent works Manolis Papadrakakis was focusing on the following fields of study:
Much of his study explores Mathematical analysis relationship to Generalization, Numerical analysis and Euler's formula.
Much of his study explores Generalization relationship to Mathematical analysis.
His study on Nanotechnology is intertwined with other disciplines of science such as Carbon nanotube and Artificial intelligence.
Manolis Papadrakakis integrates several fields in his works, including Carbon nanotube and Nanotechnology.
Borrowing concepts from Artificial neural network, he weaves in ideas under Artificial intelligence.
He merges many fields, such as Artificial neural network and Surrogate model, in his writings.
His Structural engineering study typically links adjacent topics like Retrofitting.
He frequently studies issues relating to Scale (ratio) and Quantum mechanics.
His research on Scale (ratio) frequently links to adjacent areas such as Quantum mechanics.
Between 2017 and 2022, his most popular works were:
- A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities (42 citations)
- Accurate and computationally efficient nonlinear static and dynamic analysis of reinforced concrete structures considering damage factors (18 citations)
- Non-overlapping domain decomposition solution schemes for structural mechanics isogeometric analysis (11 citations)
In his most recent research, the most cited works focused on:
- Finite element method
- Conjugate gradient method
- Timoshenko beam theory
Manolis Papadrakakis integrates many fields, such as Nanotechnology and engineering, in his works.
Manolis Papadrakakis connects Carbon nanotube with Nanotechnology in his research.
Manolis Papadrakakis merges Artificial intelligence with Artificial neural network in his research.
Manolis Papadrakakis integrates Artificial neural network and Surrogate model in his research.
Many of his studies involve connections with topics such as Reinforced concrete and Structural engineering.
Reinforced concrete is closely attributed to Structural engineering in his research.
Manolis Papadrakakis carries out multidisciplinary research, doing studies in Nonlinear system and Finite element method.
He integrates Finite element method and Isogeometric analysis in his research.
His research links Scale (ratio) with Quantum mechanics.
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