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.

• Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression (Q2020804) (← links)
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
• A variation of Broyden class methods using Householder adaptive transforms (Q2023660) (← links)
• An accelerated active-set algorithm for a quadratic semidefinite program with general constraints (Q2026764) (← links)
• Implementing and modifying Broyden class updates for large scale optimization (Q2026770) (← links)
• Constructing high-quality planar NURBS parameterization for isogeometric analysis by adjustment control points and weights (Q2029692) (← links)
• New results on superlinear convergence of classical quasi-Newton methods (Q2031938) (← links)
• Semi-parametric estimation of multivariate extreme expectiles (Q2034472) (← links)
• Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
• New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization (Q2041515) (← links)
• An effective procedure for feature subset selection in logistic regression based on information criteria (Q2044566) (← links)
• Bayesian optimization with approximate set kernels (Q2051286) (← links)
• A numerical framework for elastic surface matching, comparison, and interpolation (Q2054413) (← links)
• Two limited-memory optimization methods with minimum violation of the previous secant conditions (Q2057221) (← links)
• PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background (Q2060632) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• Iterated dynamic thresholding search for packing equal circles into a circular container (Q2077968) (← links)
• Hopping between distant basins (Q2079697) (← links)
• Semi-supervised nonparametric Bayesian modelling of spatial proteomics (Q2080769) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Rotational symmetry detection in 3D using reflectional symmetry candidates and quaternion-based rotation parameterization (Q2082604) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization (Q2088969) (← links)
• A three-term conjugate gradient method with accelerated subspace quadratic optimization (Q2089194) (← links)
• Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery (Q2095535) (← links)
• SABRINA: a stochastic subspace majorization-minimization algorithm (Q2095568) (← links)
• Several accelerated subspace minimization conjugate gradient methods based on regularization model and convergence rate analysis for nonconvex problems (Q2098802) (← links)
• Re-thinking model robustness from stability: a new insight to defend adversarial examples (Q2102317) (← links)
• Arbitrary conditional inference in variational autoencoders via fast prior network training (Q2102323) (← links)
• Physics-informed neural networks for shell structures (Q2102673) (← links)
• Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
• Self-adaptive physics-informed neural networks (Q2112437) (← links)
• Batch policy learning in average reward Markov decision processes (Q2112817) (← links)
• The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning (Q2113140) (← links)
• Diagonal BFGS updates and applications to the limited memory BFGS method (Q2114834) (← links)
• Newsvendor problems: an integrated method for estimation and optimisation (Q2116868) (← links)
• Providing a model for predicting futures contract of gold coin price by using models based on \(Z\)-numbers (Q2119828) (← links)
• The data-driven localized wave solutions of the derivative nonlinear Schrödinger equation by using improved PINN approach (Q2124077) (← links)
• A regularized limited memory BFGS method for large-scale unconstrained optimization and its efficient implementations (Q2125067) (← links)
• Penalty function-based volumetric parameterization method for isogeometric analysis (Q2127712) (← links)
• An adaptive Hessian approximated stochastic gradient MCMC method (Q2128489) (← links)
• Hybrid FEM-NN models: combining artificial neural networks with the finite element method (Q2133536) (← links)
• Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556) (← links)
• APFOS-Net: asymptotic preserving scheme for anisotropic elliptic equations with deep neural network (Q2133559) (← links)
• A parallel-in-time multiple shooting algorithm for large-scale PDE-constrained optimal control problems (Q2133606) (← links)
• A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
• Degrees of freedom for off-the-grid sparse estimation (Q2137058) (← links)
• Neural networks enforcing physical symmetries in nonlinear dynamical lattices: the case example of the Ablowitz-Ladik model (Q2140106) (← links)