Pages that link to "Item:Q4646838"
From MaRDI portal
The following pages link to Learning partial differential equations via data discovery and sparse optimization (Q4646838):
Displaying 31 items.
- Learning the flux and diffusion function for degenerate convection-diffusion equations using different types of observations (Q6492247) (← links)
- A kernel framework for learning differential equations and their solution operators (Q6496499) (← links)
- \texttt{Weak-PDE-LEARN}: a weak form based approach to discovering PDEs from noisy, limited data (Q6498485) (← links)
- A proximal alternating direction method of multipliers for DC programming with structured constraints (Q6536834) (← links)
- Learning of discrete models of variational PDEs from data (Q6543708) (← links)
- Machine discovery of partial differential equations from spatiotemporal data: a sparse Bayesian learning framework (Q6553198) (← links)
- Data-driven models of nonautonomous systems (Q6553794) (← links)
- Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems (Q6557792) (← links)
- Data-driven identification of dynamical models using adaptive parameter sets (Q6561192) (← links)
- Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: a chaotic Kuramoto-Sivashinsky test case (Q6565142) (← links)
- D2NO: efficient handling of heterogeneous input function spaces with distributed deep neural operators (Q6566058) (← links)
- Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
- Regularized least absolute deviation-based sparse identification of dynamical systems (Q6571784) (← links)
- Learning about structural errors in models of complex dynamical systems (Q6572173) (← links)
- Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE (Q6583632) (← links)
- An alternating flux learning method for multidimensional nonlinear conservation laws (Q6585306) (← links)
- Data-driven identification of stable sparse differential operators using constrained regression (Q6588307) (← links)
- Ml-GLE: a machine learning enhanced generalized Langevin equation framework for transient anomalous diffusion in polymer dynamics (Q6589873) (← links)
- Identification of partial-differential-equations-based models from noisy data with splines (Q6593371) (← links)
- Differential equations in data analysis (Q6602133) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference (Q6604850) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER) (Q6621776) (← links)
- Gaussian Process Assisted Active Learning of Physical Laws (Q6631893) (← links)
- A discretization-invariant extension and analysis of some deep operator networks (Q6633297) (← links)
- Approximation identification for the stochastic time-delayed dynamical system (Q6638497) (← links)
- Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluids (Q6641917) (← links)
- How much can one learn a partial differential equation from its solution? (Q6645956) (← links)
- Accurate data-driven surrogates of dynamical systems for forward propagation of uncertainty (Q6648584) (← links)
- Group projected subspace pursuit for block sparse signal reconstruction: convergence analysis and applications (Q6657433) (← links)
