Todd A. Oliver

Computational Mechanics · 2005–2019

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 22 citations into it; #9,737 of 10,001 by trust here, #7,173 by citations
• Statistical and Nonlinear Physics — 12 citations into it; #9,922 of 10,127 by trust here, #7,024 by citations
• Applied Mathematics — 8 citations into it
• Artificial Intelligence — 8 citations into it
• Numerical Analysis — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Statistics and Probability — 6 citations into it; #4,276 of 12,932 by trust here, #7,914 by citations
• AI for Physical Sciences — 5 citations into it

Whom Todd A. Oliver trusts overall

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

1. Albert Tarantola
2. Sergio Hoyas
3. Javier Jiménez
4. J. D. Álamo
5. Raytcho D. Lazarov
6. Bernardo Cockburn
7. Francesco Bassi
8. Xiaolin Li
9. Diederik P. Kingma
10. James L. Beck

Who trusts Todd A. Oliver overall

1. Heng Xiao
2. Nicholas Geneva
3. D. Papadimitriou
4. Anand Pratap Singh
5. Paola Cinnella
6. Nicholas Zabaras
7. Karthik Duraisamy
8. Costas Papadimitriou
9. Shivaji Medida
10. Takanori Haga

Papers from Todd A. Oliver's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Understanding the difficulty of training deep feedforward neural networks
3. Scaling of the velocity fluctuations in turbulent channels up to Reτ=2003
4. Scaling of the energy spectra of turbulent channels (Journal of Fluid Mechanics)
5. Inverse problem theory - and methods for model parameter estimation
6. ImageNet classification with deep convolutional neural networks
7. Gradient-based learning applied to document recognition
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Unified Analysis of Discontinuous Galerkin Methods for Elliptic Problems (SIAM Journal on Numerical Analysis)
10. GMRES Discontinuous Galerkin Solution of the Compressible Navier-Stokes Equations

Their papers

• Bayesian uncertainty quantification applied to RANS turbulence models (2011) — 156 citations
• Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks (2019) — 23 citations
• p-Multigrid solution of high-order discontinuous Galerkin discretizations of the compressible Navier-Stokes equations (2005) — 408 citations