gLaSDI: parametric physics-informed greedy latent space dynamics identification
From MaRDI portal
Publication:6107110
DOI10.1016/j.jcp.2023.112267arXiv2204.12005OpenAlexW4379390621MaRDI QIDQ6107110
Xiaolong He, Jonathan L. Belof, William D. Fries, Jiun-Shyan Chen, Youngsoo Choi
Publication date: 3 July 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2204.12005
nonlinear dynamical systemsadaptive samplingreduced order modelautoencodersphysics-informed greedy algorithmregression-based dynamics identification
Basic methods in fluid mechanics (76Mxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)
Related Items
Adaptive learning of effective dynamics for online modeling of complex systems ⋮ GPLaSDI: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder ⋮ Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition
Cites Work
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation
- A decomposed subspace reduction for fracture mechanics based on the meshfree integrated singular basis function method
- A practical factorization of a Schur complement for PDE-constrained distributed optimal control
- Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations
- An algorithmic comparison of the hyper-reduction and the discrete empirical interpolation method for a nonlinear thermal problem
- Projection-based model reduction: formulations for physics-based machine learning
- Stabilized reduced order models for the advection-diffusion-reaction equation using operator splitting
- Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
- Reduced order models for Lagrangian hydrodynamics
- Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations
- Projection-based model reduction of dynamical systems using space-time subspace and machine learning
- Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems
- Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena
- Gradient-based constrained optimization using a database of linear reduced-order models
- Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems
- A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
- MFEM: a modular finite element methods library
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- Component-wise reduced order model lattice-type structure design
- Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
- Data-driven operator inference for nonintrusive projection-based model reduction
- A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
- Conservative model reduction for finite-volume models
- LaSDI: parametric latent space dynamics identification
- Bayesian operator inference for data-driven reduced-order modeling
- Proper orthogonal decomposition-based model order reduction via radial basis functions for molecular dynamics systems
- Improving the efficiency of large scale topology optimization through on-the-fly reduced order model construction
- Model order reduction for meshfree solution of Poisson singularity problems
- Large‐Scale Inverse Problems and Quantification of Uncertainty
- Reducing the Dimensionality of Data with Neural Networks
- Large-scale topology optimization using preconditioned Krylov subspace methods with recycling
- A Schur method for balanced-truncation model reduction
- THE PARTITION OF UNITY METHOD
- The Shifted Proper Orthogonal Decomposition: A Mode Decomposition for Multiple Transport Phenomena
- Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction
- SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction
- Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference
- Data-driven discovery of coordinates and governing equations
- Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems
- Deep learning in fluid dynamics
- Variational multiscale proper orthogonal decomposition: Navier‐stokes equations
