Romit Maulik

Statistical and Nonlinear Physics · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 28 citations into it; #8,388 of 10,127 by trust here, #6,727 by citations
• Computational Mechanics — 20 citations into it; #8,519 of 10,001 by trust here, #6,877 by citations
• Statistics and Probability — 13 citations into it
• Artificial Intelligence — 5 citations into it

Whom Romit Maulik trusts overall

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

1. Kevin T. Carlberg
2. Kookjin Lee
3. Sepp Hochreiter
4. Michele Milano
5. G. Berkooz
6. J. Nathan Kutz
7. Dunhui Xiao
8. J. Urgen Schmidhuber
9. Hans Petter Langtangen
10. Petros Koumoutsakos

Who trusts Romit Maulik overall

1. Ricardo Vinuesa
2. Kai Fukami
3. Adil Rasheed
4. Steven L. Brunton
5. Koji Fukagata
6. Omer San
7. Kunihiko Taira

Papers from Romit Maulik's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Gradient-based learning applied to document recognition
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows
7. Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations
8. Learning Multiple Layers of Features from Tiny Images
9. Dynamic Mode Decomposition with Control (SIAM Journal on Applied Dynamical Systems)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Subgrid modelling for two-dimensional turbulence using neural networks (2018) — 341 citations
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020) — 319 citations