Ricardo Vinuesa
Computational Mechanics · 2014–2021
A researcher in the Science corpus: 7 papers; trusts 64 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 87 citations into it; #6,049 of 10,001 by trust here, #5,813 by citations
- Environmental Engineering — 46 citations into it
- Statistical and Nonlinear Physics — 41 citations into it; #9,815 of 10,127 by trust here, #9,324 by citations
- Global and Planetary Change — 21 citations into it
- Mechanical Engineering — 21 citations into it
- Aerospace Engineering — 14 citations into it; #5,179 of 6,421 by trust here, #5,172 by citations
- Artificial Intelligence — 10 citations into it
- Statistics and Probability — 8 citations into it
Whom Ricardo Vinuesa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Philipp Schlatter
- J. Nathan Kutz
- Steven L. Brunton
- Maizar Raissi
- Ramis Örlü
- Frank No'e
- John R. Koza
- Cynthia Rudin
- Jin-Long Wu
- Ivan Marusic
Who trusts Ricardo Vinuesa overall
Papers from Ricardo Vinuesa's perspective
Ordered by how strongly the researchers Ricardo Vinuesa trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Regression Shrinkage and Selection via the Lasso
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations (2021) — 445 citations
- Enhancing computational fluid dynamics with machine learning (2021) — 600 citations
- Turbulent boundary layers around wing sections up to Rec=1,000,000 (2018) — 133 citations
- History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers (2017) — 212 citations
- Direct numerical simulation of the flow around a wing section at moderate Reynolds number (2016) — 143 citations
- On determining characteristic length scales in pressure gradient turbulent boundary layers (2016) — 133 citations
- Aspect ratio effects in turbulent duct flows studied through direct numerical simulation (2014) — 144 citations