Pages that link to "Item:Q2033658"

The following pages link to Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (Q2033658):

Displaying 38 items.

• PINN (Q54699) (← links)
• Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation (Q2138688) (← links)
• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
• Space-time isogeometric analysis of car and tire aerodynamics with road contact and tire deformation and rotation (Q2150255) (← links)
• Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks (Q2156788) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Wind turbine wake computation with the ST-VMS method, isogeometric discretization and multidomain method. I: Computational framework (Q2666083) (← links)
• Machine learning constitutive models of elastomeric foams (Q2670325) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• High-resolution multi-domain space-time isogeometric analysis of car and tire aerodynamics with road contact and tire deformation and rotation (Q2683305) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Advances in Neural Networks – ISNN 2005 (Q5707446) (← links)
• Carrier-domain method for high-resolution computation of time-periodic long-wake flows (Q6042218) (← links)
• Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement (Q6044216) (← links)
• Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations (Q6051370) (← links)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data (Q6096490) (← links)
• Boundary layer mesh resolution in flow computation with the Space–Time Variational Multiscale method and isogeometric discretization (Q6099246) (← links)
• Flow computation with the Space–Time Isogeometric Analysis and higher-order basis functions in time (Q6099247) (← links)
• Complex-geometry simulations of transient thermoelasticity with the shifted boundary method (Q6118512) (← links)
• Isogeometric analysis in computation of complex-geometry flow problems with moving boundaries and interfaces (Q6125051) (← links)
• Physics-informed neural networks with two weighted loss function methods for interactions of two-dimensional oceanic internal solitary waves (Q6130987) (← links)
• Physics-informed machine-learning model of temperature evolution under solid phase processes (Q6159327) (← links)
• Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification (Q6164278) (← links)
• Variational multiscale method stabilization parameter calculated from the strain-rate tensor (Q6166565) (← links)
• Space-time computational flow analysis: unconventional methods and first-ever solutions (Q6187612) (← links)
• Space-time flow computation with boundary layer and contact representation: a 10-year history (Q6540740) (← links)
• GO-MELT: GPU-optimized multilevel execution of LPBF thermal simulations (Q6550132) (← links)
• A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)
• Deep learning-based method for solving seepage equation under unsteady boundary (Q6574145) (← links)
• PINN enhanced extended multiscale finite element method for fast mechanical analysis of heterogeneous materials (Q6576389) (← links)
• High-resolution 3D computation of time-periodic long-wake flows with the carrier-domain method and space-time variational multiscale method with isogeometric discretization (Q6584852) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• On physics-informed neural networks training for coupled hydro-poromechanical problems (Q6615008) (← links)
• Local-length-scale calculation in T-splines meshes for complex geometries (Q6633031) (← links)
• Unsupervised neural networks for Maxwell fluid flow and heat transfer over a curved surface with nonlinear convection and temperature-dependent properties (Q6660596) (← links)
• Least-square finite difference-based physics-informed neural network for steady incompressible flows (Q6663359) (← links)
• An implicit GNN solver for Poisson-like problems (Q6663428) (← links)