Pages that link to "Item:Q2135255"
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The following pages link to Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255):
Displaying 11 items.
- Learning quantities of interest from dynamical systems for observation-consistent inversion (Q2060144) (← links)
- Learning generative neural networks with physics knowledge (Q2146912) (← links)
- A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (Q2309342) (← links)
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
- Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- Physics-constrained data-driven variational method for discrepancy modeling (Q6187631) (← links)
- Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations (Q6335487) (← links)
- How much can one learn a partial differential equation from its solution? (Q6645956) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
