Pages that link to "Item:Q2310233"

The following pages link to An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications (Q2310233):

Displaying 50 items.

• A refined quasi-3D logarithmic shear deformation theory-based effective meshfree method for analysis of functionally graded plates resting on the elastic foundation (Q1980158) (← links)
• Dual BEM for wave scattering by an H-type porous barrier with nonlinear pressure drop (Q1980177) (← links)
• The smoothed finite element method for time-dependent mechanical responses of MEE materials and structures around Curie temperature (Q2020262) (← links)
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
• \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition (Q2021230) (← links)
• Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials (Q2021910) (← links)
• Interface immersed particle difference method for weak discontinuity in elliptic boundary value problems (Q2022079) (← links)
• A unified-implementation of smoothed finite element method (UI-SFEM) for simulating biomechanical responses of multi-materials orthodontics (Q2033653) (← links)
• Free vibration of irregular plates via indirect differential quadrature and singular convolution techniques (Q2040763) (← links)
• Analytical and meshless numerical approaches to unified gradient elasticity theory (Q2040853) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Mesh refinement procedures for the phase field approach to brittle fracture (Q2060115) (← links)
• Multi-fidelity meta modeling using composite neural network with online adaptive basis technique (Q2060166) (← links)
• Two novel Bessel matrix techniques to solve the squeezing flow problem between infinite parallel plates (Q2066334) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• A local meshless method for transient nonlinear problems: preliminary investigation and application to phase-field models (Q2079767) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem (Q2095152) (← links)
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
• Deep learning for gas sensing using MOFs coated weakly-coupled microbeams (Q2109926) (← links)
• A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks (Q2122243) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• Deep neural networks and adaptive quadrature for solving variational problems (Q2128466) (← links)
• A modified batch intrinsic plasticity method for pre-training the random coefficients of extreme learning machines (Q2133017) (← links)
• SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
• On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
• A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
• A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
• A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures (Q2140066) (← links)
• SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
• A mesh-free method using piecewise deep neural network for elliptic interface problems (Q2141617) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← links)
• A feed-forwarded neural network-based variational Bayesian learning approach for forensic analysis of traffic accident (Q2145145) (← links)
• A novel localized collocation solver based on a radial Trefftz basis for thermal conduction analysis in FGMs with exponential variations (Q2147296) (← links)
• Size-dependent nonlinear vibration of functionally graded composite micro-beams reinforced by carbon nanotubes with piezoelectric layers in thermal environments (Q2156205) (← links)
• Nonlinear transient thermo-elastoplastic analysis of temperature-dependent FG plates using an efficient 3D meshless model (Q2158536) (← links)
• Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
• Probabilistic deep learning for real-time large deformation simulations (Q2160483) (← links)
• Application of Haar wavelet discretization and differential quadrature methods for free vibration of functionally graded micro-beam with porosity using modified couple stress theory (Q2161600) (← links)
• Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm (Q2168118) (← links)
• Finite element analysis of shock absorption of porous soles established by Grasshopper and UG secondary development (Q2214836) (← links)
• Geometrically nonlinear postbuckling behavior of imperfect FG-CNTRC shells under axial compression using isogeometric analysis (Q2224574) (← links)
• Isogeometric analysis of bending, vibration, and buckling behaviors of multilayered microplates based on the non-classical refined shear deformation theory (Q2234588) (← links)
• A unified adaptive approach for membrane structures: form finding and large deflection isogeometric analysis (Q2236180) (← links)
• Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines (Q2236543) (← links)