Simultaneous Identification and Denoising of Dynamical Systems
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Publication:6113941
DOI10.1137/22m1486303zbMath1520.34017arXiv2203.13837OpenAlexW4383218206MaRDI QIDQ6113941
Gianluca Iaccarino, Jeffrey Hokanson, Alireza Doostan
Publication date: 11 July 2023
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2203.13837
System identification (93B30) Inverse problems involving ordinary differential equations (34A55) Methods of successive quadratic programming type (90C55) Numerical solution of inverse problems involving ordinary differential equations (65L09)
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Cites Work
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- Tensor Decompositions and Applications
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Iterative hard thresholding for compressed sensing
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares
- Numerical aspects for approximating governing equations using data
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
- Data-driven operator inference for nonintrusive projection-based model reduction
- Preconditioned all-at-once methods for large, sparse parameter estimation problems
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- A Sequential Quadratic Programming Method Without A Penalty Function or a Filter for Nonlinear Equality Constrained Optimization
- Iteratively reweighted least squares minimization for sparse recovery
- A Supernodal Approach to Sparse Partial Pivoting
- On optimization techniques for solving nonlinear inverse problems
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
- An Algorithm for Degenerate Nonlinear Programming with Rapid Local Convergence
- A Data Scalable Augmented Lagrangian KKT Preconditioner for Large-Scale Inverse Problems
- The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate