Pages that link to "Item:Q2129334"
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The following pages link to Solving inverse-PDE problems with physics-aware neural networks (Q2129334):
Displaying 13 items.
- Inversion of Fredholm integral equations of the first kind with fully connected neural networks (Q1188061) (← links)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Error-correcting neural networks for semi-Lagrangian advection in the level-set method (Q2088344) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
- JAX-DIPS: neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities (Q6048459) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- <tt>TNet</tt>: A Model-Constrained Tikhonov Network Approach for Inverse Problems (Q6154957) (← links)
- Applying artificial neural networks to solve the inverse problem of evaluating concentrations in multianalyte mixtures from biosensor signals (Q6202316) (← links)
- Solving inverse-PDE problems with physics-aware neural networks (Q6332652) (← links)
- A non-parametric gradient-based shape optimization approach for solving inverse problems in directed self-assembly of block copolymers (Q6575314) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
