Pages that link to "Item:Q911463"

The following pages link to On the limited memory BFGS method for large scale optimization (Q911463):

Displaying 50 items.

• Covariance prediction via convex optimization (Q6050386) (← links)
• SCORE: approximating curvature information under self-concordant regularization (Q6051307) (← links)
• Calibration of local‐stochastic volatility models by optimal transport (Q6054403) (← links)
• NIFT<scp>y</scp> 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters (Q6059659) (← links)
• Towards global parameter estimation exploiting reduced data sets (Q6065211) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
• Accelerated nonlinear finite element method for analysis of isotropic hyperelastic materials nonlinear deformations (Q6072709) (← links)
• A physics-constrained deep residual network for solving the sine-Gordon equation (Q6076683) (← links)
• Dimensionality reduction for regularization of sparse data-driven RANS simulations (Q6078483) (← links)
• Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations (Q6078492) (← links)
• Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics (Q6084492) (← links)
• A unifying framework for sparsity-constrained optimization (Q6086139) (← links)
• A deep learning method for solving third-order nonlinear evolution equations (Q6094544) (← links)
• Single-track thermal analysis of laser powder bed fusion process: parametric solution through physics-informed neural networks (Q6094682) (← links)
• A cusp-capturing PINN for elliptic interface problems (Q6095097) (← links)
• Regularization of limited memory quasi-Newton methods for large-scale nonconvex minimization (Q6095733) (← links)
• Artificial neural network solver for time-dependent Fokker-Planck equations (Q6096280) (← links)
• On the use of neural networks for full waveform inversion (Q6096500) (← links)
• Deep learning data-driven multi-soliton dynamics and parameters discovery for the fifth-order Kaup-Kuperschmidt equation (Q6096544) (← links)
• An overview of stochastic quasi-Newton methods for large-scale machine learning (Q6097379) (← links)
• A reduced order with data assimilation model: theory and practice (Q6100085) (← links)
• Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation (Q6103633) (← links)
• Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
• Constrained composite optimization and augmented Lagrangian methods (Q6110459) (← links)
• Deblurring photographs of characters using deep neural networks (Q6114462) (← links)
• Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance (Q6116144) (← links)
• A shallow physics-informed neural network for solving partial differential equations on static and evolving surfaces (Q6118543) (← links)
• Topology optimization for inverse magnetostatics as sparse regression: application to electromagnetic coils for stellarators (Q6118568) (← links)
• Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control (Q6119249) (← links)
• Alternating cyclic vector extrapolation technique for accelerating nonlinear optimization algorithms and fixed-point mapping applications (Q6126026) (← links)
• Loss-attentional physics-informed neural networks (Q6126561) (← links)
• Deep learning-based schemes for singularly perturbed convection-diffusion problems (Q6127045) (← links)
• Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition (Q6127062) (← links)
• nlTGCR: A Class of Nonlinear Acceleration Procedures Based on Conjugate Residuals (Q6130649) (← links)
• NSNO: Neumann series neural operator for solving Helmholtz equations in inhomogeneous medium (Q6130979) (← links)
• Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
• Parallel physics-informed neural networks method with regularization strategies for the forward-inverse problems of the variable coefficient modified KdV equation (Q6130986) (← links)
• Inexact penalty decomposition methods for optimization problems with geometric constraints (Q6133301) (← links)
• A new taxonomy of global optimization algorithms (Q6137180) (← links)
• A regularized limited memory subspace minimization conjugate gradient method for unconstrained optimization (Q6141538) (← links)
• Eigenvalue analyses on the memoryless Davidon-Fletcher-Powell method based on a spectral secant equation (Q6142074) (← links)
• Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
• Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
• \texttt{qocttools}: a program for quantum optimal control calculations (Q6147798) (← links)
• SHED: a Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing (Q6152580) (← links)
• A bundle-type method for nonsmooth DC programs (Q6154409) (← links)
• A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms (Q6154930) (← links)
• A generative model for texture synthesis based on optimal transport between feature distributions (Q6155446) (← links)