A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics
DOI10.1007/S00466-023-02288-WzbMath1525.70003OpenAlexW4323048778MaRDI QIDQ6109265
Michael D. Todd, David A. Najera-Flores
Publication date: 27 July 2023
Published in: Computational Mechanics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00466-023-02288-w
Euler-Lagrange equationsDuffing oscillatormultilayer perceptronRayleigh dampingnonlinear structural dynamicsauto-differentiationstructure-preserving machine learningsuspended mass problem
Artificial neural networks and deep learning (68T07) Forced motions for nonlinear problems in mechanics (70K40) Computational methods for problems pertaining to mechanics of particles and systems (70-08)
Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A family of embedded Runge-Kutta formulae
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Physics-informed semantic inpainting: application to geostatistical modeling
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview
- Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Model Reduction and Approximation
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