Anand Pratap Singh

Computational Mechanics · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 23 citations into it; #1,517 of 10,001 by trust here, #2,574 by citations
• Statistics and Probability — 17 citations into it; #7,507 of 12,932 by trust here, #6,310 by citations
• Statistical and Nonlinear Physics — 11 citations into it; #3,452 of 10,127 by trust here, #5,625 by citations
• Aerospace Engineering — 9 citations into it; #1,963 of 6,421 by trust here, #3,911 by citations
• Environmental Engineering — 8 citations into it; #1,587 of 5,479 by trust here, #3,627 by citations
• Applied Mathematics — 5 citations into it

Whom Anand Pratap Singh trusts overall

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

1. Jian-Xun Wang
2. Karthik Duraisamy
3. Jin-Long Wu
4. Julia Ling
5. Todd A. Oliver
6. Michele Milano
7. John E. Dennis Jr.
8. Sergios Theodoridis
9. Heng Xiao
10. Jeremy A. Templeton

Who trusts Anand Pratap Singh overall

1. Heng Xiao
2. Jin-Long Wu
3. Paola Cinnella
4. Linyang Zhu
5. Navid Zobeiry
6. Nicholas Geneva
7. Karthik Duraisamy
8. Eric Paterson
9. Jian-Xun Wang
10. Shivaji Medida

Papers from Anand Pratap Singh's perspective

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

1. A paradigm for data-driven predictive modeling using field inversion and machine learning
2. Machine learning strategies for systems with invariance properties (Journal of Computational Physics)
3. Using field inversion to quantify functional errors in turbulence closures
4. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
5. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
6. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (Journal of Computational Physics)
7. An Introduction to the Adjoint Approach to Design
8. Pattern recognition
9. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses
10. Random Forests

Their papers

• New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques (2015) — 237 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