Heng Xiao

Computational Mechanics · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 81 citations into it; #1,648 of 10,001 by trust here, #1,536 by citations
• Statistics and Probability — 58 citations into it; #4,770 of 12,932 by trust here, #2,965 by citations
• Environmental Engineering — 53 citations into it
• Statistical and Nonlinear Physics — 38 citations into it; #1,068 of 10,127 by trust here, #1,059 by citations
• Artificial Intelligence — 20 citations into it
• Aerospace Engineering — 19 citations into it
• Mechanical Engineering — 15 citations into it
• Atmospheric Science — 13 citations into it

Whom Heng Xiao trusts overall

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

1. Jeremy A. Templeton
2. Anand Pratap Singh
3. Geir Evensen
4. Karthik Duraisamy
5. Stephen B. Pope
6. Julia Ling
7. Jin-Long Wu
8. Marc C. Kennedy
9. Jack Weatheritt
10. M. Giles

Who trusts Heng Xiao overall

1. Karthik Duraisamy
2. Ricardo Vinuesa
3. Anand Pratap Singh
4. Steven L. Brunton
5. Paola Cinnella
6. Gianluca Iaccarino
7. Jin-Long Wu
8. Adil Rasheed
9. Shivaji Medida
10. Omer San

Papers from Heng Xiao's perspective

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

1. Turbulent Flows
2. Random Forests
3. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
4. Machine learning strategies for systems with invariance properties (Journal of Computational Physics)
5. The Elements of Statistical Learning
6. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations (SIAM Journal on Scientific Computing)
7. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
8. Using field inversion to quantify functional errors in turbulence closures
9. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
10. A paradigm for data-driven predictive modeling using field inversion and machine learning

Their papers

• Turbulence Modeling in the Age of Data (2018) — 1,481 citations
• Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017) — 542 citations
• Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015) — 315 citations
• Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2018) — 195 citations
• Quantification of model uncertainty in RANS simulations: A review (2018) — 392 citations
• Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016) — 668 citations