Andrea Manzoni

Statistical and Nonlinear Physics · 2011–2021

A researcher in the Science corpus: 9 papers; trusts 105 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 131 citations into it; #1,695 of 10,127 by trust here, #2,928 by citations
• Computational Mechanics — 120 citations into it; #1,436 of 10,001 by trust here, #2,715 by citations
• Statistics and Probability — 66 citations into it; #7,996 of 12,932 by trust here, #9,714 by citations
• Numerical Analysis — 40 citations into it; #999 of 3,586 by trust here, #739 by citations
• Mechanics of Materials — 37 citations into it
• Artificial Intelligence — 24 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Andrea Manzoni trusts overall

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

1. Gianluigi Rozza
2. Karen Veroy
3. Maizar Raissi
4. Anthony T. Patera
5. Alfio Maria Quarteroni
6. Martin A. Grepl
7. Dinh Bao Phuong Huynh
8. Jan S. Hesthaven
9. Yvon Maday
10. Anthony Nouy

Who trusts Andrea Manzoni overall

1. Claudio Paniconi
2. Francesco Ballarin
3. Stefano Lorenzi
4. Stefania Fresca
5. Gianluigi Rozza
6. Alfio Maria Quarteroni
7. Mario Putti
8. L. Zampieri
9. Antonio Cammi
10. Kaushik Bhattacharya

Papers from Andrea Manzoni's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Reduced Basis Methods for Partial Differential Equations: An Introduction (2015) — 1,008 citations
• A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs (2020) — 348 citations
• Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations (2015) — 289 citations
• Computational Reduction for Parametrized PDEs: Strategies and Applications (2012) — 50 citations
• POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (2021) — 307 citations
• An efficient computational framework for reduced basis approximation and a posteriori error estimation of parametrized Navier–Stokes flows (2014) — 50 citations
• Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (2014) — 291 citations
• Shape optimization for viscous flows by reduced basis methods and free‐form deformation (2012) — 143 citations
• Certified reduced basis approximation for parametrized partial differential equations and applications (2011) — 300 citations