Kevin T. Carlberg

Statistical and Nonlinear Physics · 2008–2018

A researcher in the Science corpus: 6 papers; trusts 75 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 82 citations into it; #647 of 10,127 by trust here, #989 by citations
• Computational Mechanics — 72 citations into it; #1,752 of 10,001 by trust here, #1,985 by citations
• Statistics and Probability — 42 citations into it; #1,953 of 12,932 by trust here, #1,962 by citations
• Numerical Analysis — 18 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Mechanics of Materials — 13 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Kevin T. Carlberg trusts overall

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

1. Karen E. Willcox
2. Charbel Farhat
3. Karen Veroy
4. Ngoc Cuong Nguyen
5. David Amsallem
6. Anthony T. Patera
7. Tan Bui-Thanh
8. Athanasios C. Antoulas
9. Martin A. Grepl
10. Jaime Peraire

Who trusts Kevin T. Carlberg overall

1. Charbel Farhat
2. Kookjin Lee
3. Philip Avery
4. Andrea Manzoni
5. Matthew J. Zahr
6. David Amsallem
7. Saifon Chaturantabut
8. Steven L. Brunton
9. Stefania Fresca
10. Francesco Ballarin

Papers from Kevin T. Carlberg's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• A method for interpolating on manifolds structural dynamics reduced‐order models (2009) — 257 citations
• The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows (2012) — 656 citations
• A low‐cost, goal‐oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems (2011) — 127 citations
• A Compact Proper Orthogonal Decomposition Basis for Optimization-Oriented Reduced-Order Models (2008) — 56 citations
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (2018) — 859 citations
• Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations (2011) — 714 citations