Pages that link to "Item:Q2021252"
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The following pages link to Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (Q2021252):
Displaying 19 items.
- A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
- Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery (Q2095535) (← links)
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
- Analysis of heterogeneous structures of non-separated scales using curved bridge nodes (Q2138655) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization (Q2699379) (← links)
- Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks (Q5015302) (← links)
- (Q5053337) (← links)
- Modeling design and control problems involving neural network surrogates (Q6043129) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- On the approximation of functions by tanh neural networks (Q6055124) (← links)
- Learning high frequency data via the coupled frequency predictor-corrector triangular DNN (Q6104304) (← links)
- On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
- AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems (Q6194971) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
- Error analysis for deep neural network approximations of parametric hyperbolic conservation laws (Q6590625) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
- Deep mixed residual method for solving PDE-constrained optimization problems (Q6663447) (← links)
