Pages that link to "Item:Q2670214"
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The following pages link to Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems (Q2670214):
Displaying 26 items.
- Deep learning of thermodynamics-aware reduced-order models from data (Q2021918) (← links)
- Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference (Q2106904) (← links)
- A class of weighted energy-preserving Du Fort-Frankel difference schemes for solving sine-Gordon-type equations (Q2108655) (← links)
- Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference (Q2678552) (← links)
- Operator inference for non-intrusive model reduction with quadratic manifolds (Q2679511) (← links)
- Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder (Q5886859) (← links)
- Control of port-Hamiltonian differential-algebraic systems and applications (Q6047502) (← links)
- Canonical and noncanonical Hamiltonian operator inference (Q6062434) (← links)
- Gradient-Preserving Hyper-Reduction of Nonlinear Dynamical Systems via Discrete Empirical Interpolation (Q6066424) (← links)
- A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics (Q6109265) (← links)
- Port-Hamiltonian Dynamic Mode Decomposition (Q6116390) (← links)
- Operator inference with roll outs for learning reduced models from scarce and low-quality data (Q6135185) (← links)
- Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference (Q6175122) (← links)
- Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds (Q6194167) (← links)
- Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems (Q6495874) (← links)
- Learning of discrete models of variational PDEs from data (Q6543708) (← links)
- A pressure-free long-time stable reduced-order model for two-dimensional Rayleigh-Bénard convection (Q6545632) (← links)
- Learning physics-based reduced-order models from data using nonlinear manifolds (Q6552123) (← links)
- Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems (Q6554903) (← links)
- Gradient preserving operator inference: data-driven reduced-order models for equations with gradient structure (Q6557793) (← links)
- Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling (Q6595859) (← links)
- Model reduction on manifolds: a differential geometric framework (Q6599870) (← links)
- Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles (Q6609831) (← links)
- Nonlinear embeddings for conserving Hamiltonians and other quantities with neural Galerkin schemes (Q6623695) (← links)
- Data-driven model reduction via non-intrusive optimization of projection operators and reduced-order dynamics (Q6661629) (← links)
- System stabilization with policy optimization on unstable latent manifolds (Q6663289) (← links)
