Fetching the paper…
Reading the bibliography…
A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems.
“Grammar variational autoencoder”
Matt Kusner, Brooks Paige and José Hernández-Lobato · 1954
Earlier work this paper cites.
“A two-dimensional interpolation function for irregularly-spaced data”
Donald Shepard · 1968
Earlier work this paper cites.
“A Schur method for balanced-truncation model reduction”
Michael Safonov and RY1000665 Chiang · 1989
Earlier work this paper cites.
“The proper orthogonal decomposition in the analysis of turbulent flows”
Gal Berkooz, Philip Holmes and John Lumley · 1993
Earlier work this paper cites.
“Non-linear dimensionality reduction”
David DeMers and Garrison Cottrell · 1993
Earlier work this paper cites.
“Genetic programming as a means for programming computers by natural selection”
John Koza · 1994
Earlier work this paper cites.
“The partition of unity method”
Ivo Babuška and Jens Melenk · 1997
Earlier work this paper cites.
“Nonlinear dimensionality reduction by locally linear embedding”
Sam Roweis and Lawrence Saul · 2000
Earlier work this paper cites.
“Diverse ensembles for active learning”
Prem Melville and Raymond Mooney · 2004
Earlier work this paper cites.
“Scattered data approximation”
Holger Wendland · 2004
Earlier work this paper cites.
“Reducing the dimensionality of data with neural networks”
Geoffrey Hinton and Ruslan Salakhutdinov · 2006
Earlier work this paper cites.
“Building surrogate models based on detailed and approximate simulations”
Zhiguang Qian et al · 2006
Earlier work this paper cites.
“Large-scale topology optimization using preconditioned Krylov subspace methods with recycling”
Shun Wang, Eric Sturler and Glaucio Paulino · 2007
Earlier work this paper cites.
“Reduced basis approximation and a posteriori error estimation for parametrized partial differential equations”
Anthony Patera and Gianluigi Rozza · 2007
Earlier work this paper cites.
“Hydraulic turbine diffuser shape optimization by multiple surrogate model approximations of Pareto fronts”
B Daniel et al · 2007
Earlier work this paper cites.
“Distilling free-form natural laws from experimental data”
Michael Schmidt and Hod Lipson · 2009
Earlier work this paper cites.
“Active learning literature survey”
Burr Settles · 2009
Earlier work this paper cites.
“Non-linear model reduction for uncertainty quantification in large-scale inverse problems”
David Galbally, Krzysztof Fidkowski, Karen Willcox and Omar Ghattas · 2010
Earlier work this paper cites.
“Large-scale inverse problems and quantification of uncertainty”
George Biros et al · 2011
Earlier work this paper cites.
“Hierarchical kriging model for variable-fidelity surrogate modeling”
Zhong-Hua Han and Stefan Görtz · 2012
Earlier work this paper cites.
“Uncertainty quantification: theory, implementation, and applications”
Ralph Smith · 2013
Earlier work this paper cites.
“Proper orthogonal decomposition-based model order reduction via radial basis functions for molecular dynamics systems”
Chung-Hao Lee and Jiun-Shyan Chen · 2013
Earlier work this paper cites.
“RBF-POD reduced-order modeling of DNA molecules under stretching and bending”
Chung-Hao Lee and Jiun-Shyan Chen · 2013
Earlier work this paper cites.
“Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function”
Zhong-Hua Han, Stefan Görtz and Ralf Zimmermann · 2013
Earlier work this paper cites.
“Variational multiscale proper orthogonal decomposition: Navier-stokes equations”
Traian Iliescu and Zhu Wang · 2014
Earlier work this paper cites.
“A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation”
Qizhi He, Zhan Kang and Yiqiang Wang · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“A practical factorization of a Schur complement for PDE-constrained distributed optimal control”
Youngsoo Choi, Charbel Farhat, Walter Murray and Michael Saunders · 2015
Earlier work this paper cites.
“Model order reduction for meshfree solution of Poisson singularity problems”
Jiun-Shyan Chen, Camille Marodon and Hsin-Yun Hu · 2015
Earlier work this paper cites.
“Improving the efficiency of large scale topology optimization through on-the-fly reduced order model construction”
Christian Gogu · 2015
Earlier work this paper cites.
“Hull form optimization for reduced drag and improved seakeeping using a surrogate-based method”
Fuxin Huang, Lijue Wang and Chi Yang · 2015
Earlier work this paper cites.
“Stabilized reduced order models for the advection–diffusion–reaction equation using operator splitting”
Benjamin McLaughlin, Janet Peterson and Ming Ye · 2016
Earlier work this paper cites.
“Discovering governing equations from data by sparse identification of nonlinear dynamical systems”
Steven Brunton, Joshua Proctor and J Kutz · 2016
Earlier work this paper cites.
“Data-driven operator inference for nonintrusive projection-based model reduction”
Benjamin Peherstorfer and Karen Willcox · 2016
Earlier work this paper cites.
“ { \{ TensorFlow } \} : A System for { \{ Large-Scale } \} Machine Learning”
Martı́n Abadi et al · 2016
Cited alongside, same era.
“Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows”
Rambod Mojgani and Maciej Balajewicz · 2017
Cited alongside, same era.
“Deep learning in fluid dynamics”
J Kutz · 2017
Cited alongside, same era.
“Deep learning in bioinformatics”
Seonwoo Min, Byunghan Lee and Sungroh Yoon · 2017
Cited alongside, same era.
“Automatic differentiation in pytorch”, 2017
Adam Paszke et al · 2017
Cited alongside, same era.
“Neural architecture search: A survey”
Thomas Elsken, Jan Metzen and Frank Hutter · 2017
Cited alongside, same era.
“PySR: Fast & parallelized symbolic regression in Python/Julia”
M Cranmer · 2020
Later among the works it cites.
“Sampling low-dimensional Markovian dynamics for preasymptotically recovering reduced models from data with operator inference”
Benjamin Peherstorfer · 2020
Later among the works it cites.
“Learning physics-based reduced-order models for a single-injector combustion process”
Renee Swischuk, Boris Kramer, Cheng Huang and Karen Willcox · 2020
Later among the works it cites.
“A physics-constrained data-driven approach based on locally convex reconstruction for noisy database”
Qizhi He and Jiun-Shyan Chen · 2020
Later among the works it cites.
“Review of digital twin about concepts, technologies, and industrial applications”
Mengnan Liu, Shuiliang Fang, Huiyue Dong and Cunzhi Xu · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“An algorithmic comparison of the hyper-reduction and the discrete empirical interpolation method for a nonlinear thermal problem”
Felix Fritzen, Bernard Haasdonk, David Ryckelynck and Sebastian Schöps · 2018
Cited alongside, same era.
“Conservative model reduction for finite-volume models”
Kevin Carlberg, Youngsoo Choi and Syuzanna Sargsyan · 2018
Cited alongside, same era.
“Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations”
Giovanni Stabile and Gianluigi Rozza · 2018
Cited alongside, same era.
“The shifted proper orthogonal decomposition: A mode decomposition for multiple transport phenomena”
Julius Reiss, Philipp Schulze, Jörn Sesterhenn and Volker Mehrmann · 2018
Cited alongside, same era.
“Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel”
Gustavo Tapia et al · 2018
Cited alongside, same era.
“CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks”
Michela Paganini, Luke de Oliveira and Benjamin Nachman · 2018
Cited alongside, same era.
Chi Hoang, Youngsoo Choi and Kevin Carlberg · 2021
Later among the works it cites.
“Efficient space–time reduced order model for linear dynamical systems in Python using less than 120 lines of code”
Youngkyu Kim, Karen Wang and Youngsoo Choi · 2021
Later among the works it cites.
“Space–time reduced order model for large-scale linear dynamical systems with application to boltzmann transport problems”
Youngsoo Choi et al · 2021
Later among the works it cites.
“A hyper-reduction computational method for accelerated modeling of thermal cycling-induced plastic deformations”
Shigeki Kaneko et al · 2021
Later among the works it cites.
“Component-wise reduced order model lattice-type structure design”
Sean McBane and Youngsoo Choi · 2021
Later among the works it cites.
“Dynamic mode decomposition for construction of reduced-order models of hyperbolic problems with shocks”
Hannah Lu and Daniel Tartakovsky · 2021
Later among the works it cites.
Marzieh Mirhoseini and Matthew Zahr · 2021
Later among the works it cites.
“A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks”
Teeratorn Kadeethum et al · 2021
Later among the works it cites.
T Kadeethum et al · 2021
Later among the works it cites.
“Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators”
Zhe Bai and Liqian Peng · 2021
Later among the works it cites.
“Data-driven reduced-order models via regularised operator inference for a single-injector combustion process”
Shane McQuarrie, Cheng Huang and Karen Willcox · 2021
Later among the works it cites.
“Learning reduced-order dynamics for parametrized shallow water equations from data”
Süleyman Yıldız, Pawan Goyal, Peter Benner and Bülent Karasözen · 2021
Later among the works it cites.
Shane McQuarrie, Parisa Khodabakhshi and Karen Willcox · 2021
Later among the works it cites.
“Performance comparison of data-driven reduced models for a single-injector combustion process”
Parikshit Jain, Shane McQuarrie and Boris Kramer · 2021
Later among the works it cites.
“Deep autoencoders for physics-constrained data-driven nonlinear materials modeling”
Xiaolong He, Qizhi He and Jiun-Shyan Chen · 2021
Later among the works it cites.
“MFEM: A modular finite element methods library”
Robert Anderson et al · 2021
Later among the works it cites.
“A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder”
Youngkyu Kim, Youngsoo Choi, David Widemann and Tarek Zohdi · 2022
Closest in time.
“Reduced order models for Lagrangian hydrodynamics”
Dylan Copeland, Siu Cheung, Kevin Huynh and Youngsoo Choi · 2022
Closest in time.
Siu Cheung, Youngsoo Choi, Dylan Copeland and Kevin Huynh · 2022
Closest in time.
“S-OPT: A points selection algorithm for hyper-reduction in reduced order models”
Jessica Lauzon et al · 2022
Closest in time.
“LaSDI: Parametric latent space dynamics identification”
William Fries, Xiaolong He and Youngsoo Choi · 2022
Closest in time.
“Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques”
Teeratorn Kadeethum et al · 2022
Closest in time.
“Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning”
Teeratorn Kadeethum et al · 2022
Closest in time.
“Projection-based model reduction of dynamical systems using space–time subspace and machine learning”
Chi Hoang, Kenny Chowdhary, Kookjin Lee and Jaideep Ray · 2022
Closest in time.
“Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations”
Parisa Khodabakhshi and Karen Willcox · 2022
Closest in time.
“Operator inference for non-intrusive model reduction with nonlinear manifolds”
Rudy Geelen, Stephen Wright and Karen Willcox · 2022
Closest in time.
“Bayesian operator inference for data-driven reduced order modeling”
Mengwu Guo, Shane McQuarrie and Karen Willcox · 2022
Closest in time.
“Localized non-intrusive reduced-order modeling in the operator inference framework”
Rudy Geelen and Karen Willcox · 2022
Closest in time.
“Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via Shifted Operator Inference”
Opal Issan and Boris Kramer · 2022
Closest in time.