Eric Paterson

Computational Mechanics · 2001–2017

A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

Whom Eric Paterson trusts overall

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

  1. Jeremy A. Templeton
  2. Anand Pratap Singh
  3. Julia Ling
  4. Karthik Duraisamy
  5. Naomi S. Altman
  6. Trevor J. Hastie
  7. Jack Weatheritt

Who trusts Eric Paterson overall

  1. R. Wilson
  2. Frederick Stern
  3. Pablo M. Carrica
  4. Ricardo Vinuesa

Papers from Eric Paterson's perspective

Ordered by how strongly the researchers Eric Paterson 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. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
  3. Random Forests
  4. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
  5. Machine learning strategies for systems with invariance properties (Journal of Computational Physics)
  6. Using field inversion to quantify functional errors in turbulence closures
  7. A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship (Journal of Computational Physics)
  8. The Elements of Statistical Learning
  9. Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system
  10. Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (Journal of Fluid Mechanics)

Their papers