An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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Simulations demonstrate that sinusoidal thermal boundary conditions reduce entropy generation in power-law fluid natural convection relative to uniform heating, with shear-thinning fluids producing stronger buoyancy-driven flow and higher Nusselt numbers.
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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations
An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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Effects of Thermal Boundary Conditions on Natural Convection and Entropy Generation in Non-Newtonian Power-Law Fluids
Simulations demonstrate that sinusoidal thermal boundary conditions reduce entropy generation in power-law fluid natural convection relative to uniform heating, with shear-thinning fluids producing stronger buoyancy-driven flow and higher Nusselt numbers.