Paola Cinnella

Computational Mechanics · 2004–2019

A researcher in the Science corpus: 3 papers; trusts 44 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Paola Cinnella trusts overall

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

  1. Geir Evensen
  2. Anand Pratap Singh
  3. Jeremy A. Templeton
  4. Stephen B. Pope
  5. P. Durbin
  6. Paul Glasserman
  7. M. Giles
  8. Denise Draper
  9. Habib N. Najm
  10. Heng Xiao

Papers from Paola Cinnella's perspective

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

  1. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
  2. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations (SIAM Journal on Scientific Computing)
  3. Machine learning strategies for systems with invariance properties (Journal of Computational Physics)
  4. The Ensemble Kalman Filter: theoretical formulation and practical implementation
  5. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
  6. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
  7. Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
  8. Bayesian calibration of computer models
  9. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (Journal of Computational Physics)
  10. A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship (Journal of Computational Physics)

Their papers