John T. Hwang
Computational Theory and Mathematics · 2013–2019
A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 23 citations into it; #4,036 of 12,182 by trust here, #6,165 by citations
- Statistics and Probability — 20 citations into it; #4,834 of 12,932 by trust here, #6,552 by citations
- Computational Mechanics — 17 citations into it
- Civil and Structural Engineering — 10 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
- Numerical Analysis — 8 citations into it; #1,188 of 3,586 by trust here, #1,831 by citations
- Artificial Intelligence — 6 citations into it
- Industrial and Manufacturing Engineering — 5 citations into it
Whom John T. Hwang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Joaquim R. Martins
- Uwe Naumann
- Andrew B. Lambe
- Kai A. James
- Gaetan K. W. Kenway
- Zhoujie Lyu
- Laurent Hascoët
- George Biros
- Juan José Palacios Alonso
- M. Powell
Who trusts John T. Hwang overall
Papers from John T. Hwang's perspective
Ordered by how strongly the researchers John T. Hwang trusts endorse each paper. Same corpus, a different order for every researcher.
- The complex-step derivative approximation (TOMS)
- Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes (Structural And Multidisciplinary Optimization)
- A COMPARISON OF THE CONTINUOUS AND DISCRETE ADJOINT APPROACH TO AUTOMATIC AERODYNAMIC OPTIMIZATION
- Multipoint and Multi-Objective Aerodynamic Shape Optimization
- The Art of Differentiating Computer Programs - An Introduction to Algorithmic Differentiation (Software, environments, tools)
- Improved constraint-aggregation methods
- An Introduction to the Adjoint Approach to Design
- Stress-constrained topology optimization with design-dependent loading (Structural And Multidisciplinary Optimization)
- Automatic Differentiation Adjoint of the Reynolds-Averaged Navier-Stokes Equations with a Turbulence Model
- Review of options for structural design sensitivity analysis. Part 1: Linear systems
Their papers
- Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013) — 314 citations
- OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization (2019) — 610 citations
- A Python surrogate modeling framework with derivatives (2019) — 325 citations