Benjamin Peherstorfer
Benjamin Peherstorfer
Courant Institute of Mathematical Sciences, New York University
Potvrđena adresa e-pošte na cims.nyu.edu - Početna stranica
NaslovCitiranoGodina
Survey of multifidelity methods in uncertainty propagation, inference, and optimization
B Peherstorfer, K Willcox, M Gunzburger
SIAM Review 60 (3), 550-591, 2018
187*2018
Localized discrete empirical interpolation method
B Peherstorfer, D Butnaru, K Willcox, HJ Bungartz
SIAM Journal on Scientific Computing 36 (1), 2014
1312014
Dynamic data-driven reduced-order models
B Peherstorfer, K Willcox
Computer Methods in Applied Mechanics and Engineering 291, 21-41, 2015
802015
Optimal model management for multifidelity Monte Carlo estimation
B Peherstorfer, K Willcox, M Gunzburger
SIAM Journal on Scientific Computing 38 (5), A3163-A3194, 2016
722016
Data-driven operator inference for nonintrusive projection-based model reduction
B Peherstorfer, K Willcox
Computer Methods in Applied Mechanics and Engineering 306, 196-215, 2016
662016
Spatially adaptive sparse grids for high-dimensional data-driven problems
D Pflüger, B Peherstorfer, HJ Bungartz
Journal of Complexity 26 (5), 508-522, 2010
632010
Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates
B Peherstorfer, K Willcox
SIAM Journal on Scientific Computing 37 (4), A2123-A2150, 2015
612015
Multifidelity importance sampling
B Peherstorfer, T Cui, Y Marzouk, K Willcox
Computer Methods in Applied Mechanics and Engineering, 2015
552015
Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods
B Bohn, J Garcke, R Iza-Teran, A Paprotny, B Peherstorfer, ...
Procedia Computer Science 18, 621-630, 2013
412013
Projection-based model reduction: Formulations for physics-based machine learning
R Swischuk, L Mainini, B Peherstorfer, K Willcox
Computers & Fluids 179, 704-717, 2019
252019
Density Estimation with Adaptive Sparse Grids for Large Data Sets
B Peherstorfer, D Pflüge, HJ Bungartz
SIAM Data Mining 2014, 2014
222014
Model Order Reduction of Parametrized Systems with Sparse Grid Learning Techniques
B Peherstorfer
Technische Universität München, 2013
222013
Model reduction with the reduced basis method and sparse grids
B Peherstorfer, S Zimmer, HJ Bungartz
Sparse grids and applications, 223-242, 2012
192012
Multifidelity Monte Carlo estimation of variance and sensitivity indices
E Qian, B Peherstorfer, D O'Malley, VV Vesselinov, K Willcox
SIAM/ASA Journal on Uncertainty Quantification 6 (2), 683-706, 2018
172018
Geometric subspace updates with applications to online adaptive nonlinear model reduction
R Zimmermann, B Peherstorfer, K Willcox
SIAM Journal on Matrix Analysis and Applications 39 (1), 234-261, 2018
172018
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models
B Peherstorfer, B Kramer, K Willcox
Journal of Computational Physics, 2017
152017
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems
B Peherstorfer, K Willcox
Procedia Computer Science 51, 2553-2562, 2015
152015
A sparse-grid-based out-of-sample extension for dimensionality reduction and clustering with laplacian eigenmaps
B Peherstorfer, D Pflüger, HJ Bungartz
Australasian Joint Conference on Artificial Intelligence, 112-121, 2011
152011
Data-Driven Reduced Model Construction with Time-Domain Loewner Models
B Peherstorfer, S Gugercin, K Willcox
SIAM Journal on Scientific Computing 39 (5), A2152-A2178, 2017
142017
Clustering based on density estimation with sparse grids
B Peherstorfer, D Pflüger, HJ Bungartz
KI 2012: Advances in Artificial Intelligence, 131-142, 2012
132012
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