World's Best Scientists 2026 revealed!
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Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
81
Citations
21979
World Ranking
197
National Ranking
95

Research.com Recognitions

  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award

Overview

Joaquim R. R. A. Martins is affiliated with the University of Michigan-Ann Arbor in the United States. Their research is predominantly situated within the field of engineering, with a substantial focus on computational mechanics and aerospace engineering. The scientist's work extends to related areas such as global and planetary change, statistical and nonlinear physics, and computational theory and mathematics.

Their primary research topics include:

  • Computational Fluid Dynamics and Aerodynamics
  • Advanced Aircraft Design and Technologies
  • Model Reduction and Neural Networks
  • Fluid Dynamics and Turbulent Flows
  • Probabilistic and Robust Engineering Design
  • Wind Energy Research and Development
  • Advanced Multi-Objective Optimization Algorithms

Joaquim R. R. A. Martins has contributed to various academic venues, frequently publishing in:

  • AIAA Journal
  • Journal of Aircraft
  • AIAA SCITECH 2023 Forum
  • Structural and Multidisciplinary Optimization
  • arXiv (Cornell University)

Their recent published papers include:

  • "Machine learning in aerodynamic shape optimization", 2022, Progress in Aerospace Sciences
  • "Hydrogen-powered aircraft: Fundamental concepts, key technologies, and environmental impacts", 2023, Progress in Aerospace Sciences
  • "pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems", 2020, The Journal of Open Source Software
  • "Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling", 2021, Aerospace Science and Technology
  • "ADflow: An Open-Source Computational Fluid Dynamics Solver for Aerodynamic and Multidisciplinary Optimization", 2020, Journal of Aerospace Information Systems

The scientist has collaborated frequently with several co-authors, including:

  • Charles A. Mader
  • Anıl Yıldırım
  • Yingqian Liao
  • Sicheng He
  • Marco Mangano

Joaquim R. R. A. Martins has also authored a book titled Engineering Design Optimization, published in 2021 by Cambridge University Press.

Best Publications

  • Multidisciplinary design optimization: A survey of architectures

    Joaquim R. R. A. Martins;Andrew B. Lambe

  • The complex-step derivative approximation

    Joaquim R. R. A. Martins;Peter Sturdza;Juan J. Alonso

  • OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization

    Justin S. Gray;John T. Hwang;Joaquim R. R. A. Martins;Kenneth T. Moore

  • Electric, hybrid, and turboelectric fixed-wing aircraft: A review of concepts, models, and design approaches

    Benjamin J. Brelje;Joaquim R.R.A. Martins

  • Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes

    Andrew B. Lambe;Joaquim R. Martins

  • pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

    Ruben E. Perez;Peter W. Jansen;Joaquim R. Martins

  • Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

    Gaetan K. W. Kenway;Joaquim R. R. A. Martins

  • High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet

    Joaquim R. R. A. Martins;Juan J. Alonso;James J. Reuther

  • Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

    Joaquim R. R. A. Martins;John T. Hwang

  • A Python surrogate modeling framework with derivatives

    Mohamed Amine Bouhlel;John T. Hwang;Nathalie Bartoli;Rémi Lafage

  • Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark

    Zhoujie Lyu;Gaetan K. W. Kenway;Joaquim R. R. A. Martins

  • Machine Learning in Aerodynamic Shape Optimization

    Unknown

  • Scalable Parallel Approach for High-Fidelity Steady-State Aeroelastic Analysis and Adjoint Derivative Computations

    Gaetan K. W. Kenway;Graeme J. Kennedy;Joaquim R. R. A. Martins

  • A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design

    Joaquim R.R.A. Martins;Juan J. Alonso;James J. Reuther

  • Effective adjoint approaches for computational fluid dynamics

    Gaetan K.W. Kenway;Charles A. Mader;Ping He;Joaquim R.R.A. Martins

  • Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

    Zhoujie Lyu;Joaquim R. R. A. Martins

  • An adaptive approach to constraint aggregation using adjoint sensitivity analysis

    Nicholas M. K. Poon;Joaquim R. R. A. Martins

  • A CAD-Free Approach to High-Fidelity Aerostructural Optimization

    Gaetan K.W. Kenway;Graeme J. Kennedy;Joaquim R.R.A. Martins

  • AN AUTOMATED METHOD FOR SENSITIVITY ANALYSIS USING COMPLEX VARIABLES

    Joaquim R. R. A. Martins;Ilan M. Kroo;Juan J. Alonso

  • Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy

    T. Ashuri;M.B. Zaaijer;J.R.R.A. Martins;G.J.W. van Bussel

  • ADjoint: An Approach for the Rapid Development of Discrete Adjoint Solvers

    Charles A. Mader;Joaquim R. R. A. Martins;Juan J. Alonso;Edwin van der Weide

Frequent Co-Authors

Wei Shyy
Wei Shyy Hong Kong University of Science and Technology
Yin Lu Young
Yin Lu Young University of Michigan–Ann Arbor
Ann Marie Sastry
Ann Marie Sastry University of Michigan–Ann Arbor
Timothy W. Simpson
Timothy W. Simpson Pennsylvania State University
Karen Willcox
Karen Willcox The University of Texas at Austin
David W. Zingg
David W. Zingg University of Toronto
Peretz P. Friedmann
Peretz P. Friedmann University of Michigan–Ann Arbor
Niels N. Sørensen
Niels N. Sørensen Technical University of Denmark
Jie Zhang
Jie Zhang The University of Texas at Dallas
Anthony M. Waas
Anthony M. Waas Arizona State University

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