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Nonlinear Embeddings for Conserving Hamiltonians and Other Quantities with Neural Galerkin Schemes
Paul Schwerdtner, Philipp Schulze, Jules Berman and Benjamin Peherstorfer SIAM Journal on Scientific Computing 46(5) C583 (2024) https://doi.org/10.1137/23M1607799
Optimization on the symplectic Stiefel manifold: SR decomposition-based retraction and applications
Macro-micro decomposition for consistent and conservative model order reduction of hyperbolic shallow water moment equations: a study using POD-Galerkin and dynamical low-rank approximation
Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems
Rodrigo Singh, Wayne Isaac Tan Uy and Benjamin Peherstorfer Chaos: An Interdisciplinary Journal of Nonlinear Science 33(11) (2023) https://doi.org/10.1063/5.0169392
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
Harsh Sharma, Hongliang Mu, Patrick Buchfink, Rudy Geelen, Silke Glas and Boris Kramer Computer Methods in Applied Mechanics and Engineering 417 116402 (2023) https://doi.org/10.1016/j.cma.2023.116402
Energy-Conserving Hyper-Reduction and Temporal Localization for Reduced Order Models of the Incompressible Navier-Stokes Equations
Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks
Nicola Rares Franco, Stefania Fresca, Filippo Tombari and Andrea Manzoni Chaos: An Interdisciplinary Journal of Nonlinear Science 33(12) (2023) https://doi.org/10.1063/5.0170101
A robust collision source method for rank adaptive dynamical low-rank approximation in radiation therapy