Pages that link to "Item:Q5243183"
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The following pages link to Deep Learning: An Introduction for Applied Mathematicians (Q5243183):
Displaying 50 items.
- A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations (Q2034897) (← links)
- Classification with Runge-Kutta networks and feature space augmentation (Q2063032) (← links)
- LSPIA, (stochastic) gradient descent, and parameter correction (Q2074869) (← links)
- PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries (Q2128373) (← links)
- Machine learning and reduced order computation of a friction stir welding model (Q2133689) (← links)
- Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
- \(S\)-frame discrepancy correction models for data-informed Reynolds stress closure (Q2134488) (← links)
- Machine learning moment closure models for the radiative transfer equation. I: Directly learning a gradient based closure (Q2135258) (← links)
- A data-driven shock capturing approach for discontinuous Galerkin methods (Q2166588) (← links)
- A deep learning approach to the inversion of borehole resistivity measurements (Q2192773) (← links)
- Computational methods for deep learning. Theoretic, practice and applications (Q2214275) (← links)
- Adaptive non-intrusive reduced order modeling for compressible flows (Q2222527) (← links)
- Deep learning as optimal control problems: models and numerical methods (Q2297872) (← links)
- Discovering phase field models from image data with the pseudo-spectral physics informed neural networks (Q2667357) (← links)
- Control on the manifolds of mappings with a view to the deep learning (Q2676673) (← links)
- Deep limits of residual neural networks (Q2679108) (← links)
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- Deep CNNs as universal predictors of elasticity tensors in homogenization (Q2679501) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- DeepBND: a machine learning approach to enhance multiscale solid mechanics (Q2687559) (← links)
- Neural ODEs as the deep limit of ResNets with constant weights (Q4995042) (← links)
- Bilevel optimization, deep learning and fractional Laplacian regularization with applications in tomography (Q5000616) (← links)
- (Q5019109) (← links)
- Matching Component Analysis for Transfer Learning (Q5027030) (← links)
- On a multilevel Levenberg–Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations (Q5038185) (← links)
- Generalization Error Analysis of Neural Networks with Gradient Based Regularization (Q5045671) (← links)
- Deep Unfitted Nitsche Method for Elliptic Interface Problems (Q5077697) (← links)
- Sublinear Convergence of a Tamed Stochastic Gradient Descent Method in Hilbert Space (Q5093647) (← links)
- Mathematical Aspects of Deep Learning (Q5097615) (← links)
- Book Reviews (Q5140619) (← links)
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks (Q5163210) (← links)
- Solving inverse problems using data-driven models (Q5230520) (← links)
- Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method (Q5886392) (← links)
- An introduction to deep generative modeling (Q6068234) (← links)
- A literature survey of matrix methods for data science (Q6068265) (← links)
- Using deep neural networks for detecting spurious oscillations in discontinuous Galerkin solutions of convection-dominated convection-diffusion equations (Q6080848) (← links)
- Time discretization in the solution of parabolic PDEs with ANNs (Q6096361) (← links)
- Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
- Knowledge-informed neuro-integrators for aggregation kinetics (Q6121869) (← links)
- Computational Methods for Deep Learning (Q6143350) (← links)
- Machine learning architectures for price formation models (Q6166250) (← links)
- Supervised time series classification for anomaly detection in subsea engineering (Q6500018) (← links)
- A new decision making method for selection of optimal data using the von Neumann-Morgenstern theorem (Q6549298) (← links)
- Long term dynamics of the subgradient method for Lipschitz path differentiable functions (Q6566415) (← links)
- An accelerated inexact Newton regularization scheme with a learned feature-selection rule for non-linear inverse problems (Q6581199) (← links)
- Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
- Can neural networks learn finite elements? (Q6591550) (← links)
- Deep learning methods for limited data problems in X-ray tomography (Q6606476) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- Forecasting natural gas prices with spatio-temporal copula-based time series models (Q6609962) (← links)
