Karthik Duraisamy

Computational Mechanics · 2013–2019

A researcher in the Science corpus: 9 papers; trusts 55 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 144 citations into it; #1,026 of 10,001 by trust here, #876 by citations
• Statistical and Nonlinear Physics — 88 citations into it; #812 of 10,127 by trust here, #963 by citations
• Statistics and Probability — 45 citations into it; #1,894 of 12,932 by trust here, #1,662 by citations
• Aerospace Engineering — 35 citations into it; #1,789 of 6,421 by trust here, #2,193 by citations
• Artificial Intelligence — 29 citations into it
• Environmental Engineering — 24 citations into it; #718 of 5,479 by trust here, #1,555 by citations
• Control and Systems Engineering — 17 citations into it
• Applied Mathematics — 13 citations into it

Whom Karthik Duraisamy trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jeremy A. Templeton
2. M. Giles
3. Jin-Long Wu
4. Heng Xiao
5. Jian-Xun Wang
6. Bharathram Ganapathisubramani
7. Jack Weatheritt
8. Anand Pratap Singh
9. Julia Ling
10. M. Shur

Who trusts Karthik Duraisamy overall

1. Heng Xiao
2. Jin-Long Wu
3. Linyang Zhu
4. Zichao Long
5. Anand Pratap Singh
6. Jian-Xun Wang
7. Ricardo Vinuesa
8. Steven L. Brunton
9. Weiwei Zhang
10. Yiping Lu

Papers from Karthik Duraisamy's perspective

Ordered by how strongly the researchers Karthik Duraisamy trusts endorse each paper. Same corpus, a different order for every researcher.

1. Turbulent Flows
2. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
3. Machine learning strategies for systems with invariance properties (Journal of Computational Physics)
4. A paradigm for data-driven predictive modeling using field inversion and machine learning
5. Direct numerical simulation of impinging shock wave/turbulent boundary layer interaction at M=2.25
6. Fifty Years of Shock-Wave/Boundary-Layer Interaction Research: What Next?
7. Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
8. Effects of upstream boundary layer on the unsteadiness of shock-induced separation (Journal of Fluid Mechanics)
9. Using field inversion to quantify functional errors in turbulence closures
10. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

Their papers

• A paradigm for data-driven predictive modeling using field inversion and machine learning (2016) — 593 citations
• Turbulence Modeling in the Age of Data (2018) — 1,481 citations
• New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques (2015) — 237 citations
• Prediction of aerodynamic flow fields using convolutional neural networks (2019) — 631 citations
• Modal Analysis of Fluid Flows: Applications and Outlook (2019) — 515 citations
• Using field inversion to quantify functional errors in turbulence closures (2016) — 217 citations
• Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils (2016) — 371 citations
• Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design (2013) — 501 citations
• Flow physics and RANS modelling of oblique shock/turbulent boundary layer interaction (2013) — 143 citations