Benjamin Peherstorfer

Statistics and Probability · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 130 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 105 citations into it; #2,042 of 12,932 by trust here, #2,579 by citations
• Statistical and Nonlinear Physics — 99 citations into it; #1,028 of 10,127 by trust here, #2,036 by citations
• Numerical Analysis — 42 citations into it; #1,417 of 3,586 by trust here, #1,968 by citations
• Computational Theory and Mathematics — 41 citations into it; #2,095 of 12,182 by trust here, #3,117 by citations
• Artificial Intelligence — 40 citations into it
• Computational Mechanics — 39 citations into it
• Control and Systems Engineering — 29 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Benjamin Peherstorfer trusts overall

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

1. Athanasios C. Antoulas
2. Alexander I. J. Forrester
3. Karen E. Willcox
4. Serkan Gugercin
5. M. Giles
6. Anthony Nouy
7. Radford M. Neal
8. Stefan Heinrich
9. Gareth O. Roberts
10. Saifon Chaturantabut

Who trusts Benjamin Peherstorfer overall

1. Xianglin Liu
2. Karen E. Willcox
3. Ricardo Vinuesa
4. Yibo Yang
5. M. Giselle Fernández-Godino
6. Rohit Tripathy
7. Dehao Liu
8. Renee C. Swischuk
9. Paris Perdikaris
10. Steven L. Brunton

Papers from Benjamin Peherstorfer's perspective

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

1. Balanced Model Reduction via the Proper Orthogonal Decomposition
2. Statistical Learning Theory
3. EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS
4. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows
5. Design and analysis of computer experiments
6. Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space (SIAM Journal on Scientific Computing)
7. An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations
8. Approximation of Large-Scale Dynamical Systems (Advances in Design and Control)
9. Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics (SIAM Journal on Numerical Analysis)
10. Efficient Global Optimization of Expensive Black-Box Functions

Their papers

• Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018) — 938 citations
• Optimal Model Management for Multifidelity Monte Carlo Estimation (2016) — 257 citations
• Data-driven operator inference for nonintrusive projection-based model reduction (2016) — 512 citations
• Multifidelity importance sampling (2016) — 140 citations
• Projection-based model reduction: Formulations for physics-based machine learning (2019) — 333 citations
• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (2019) — 316 citations