A Unified Variational Functional for Equidistribution and Alignment in Moving Mesh Adaptation

Existing variational mesh functionals often suffer from strong nonlinearity or dependence on empirical parameters.We propose a new variational functional for adaptive moving mesh generation that enforces equidistribution and alignment through an $\mathit{A}$-pullback formulation, where $\mathit{A}={\mathit{J}}^{-1}{\mathit{M}}^{-1}{\mathit{J}}^{-T}$. The functional combines a trace-based term with a logarithmic determinant term, achieving balanced control of mesh size and anisotropy without empirical parameters. We establish coercivity, polyconvexity, existence of minimizers, and geodesic convexity with respect to the inverse Jacobian, and derive a simplified geometric discretization leading to an efficient moving mesh algorithm. Numerical experiments confirm the theoretical properties and demonstrate robust adaptive behavior for function-induced meshes and Rayleigh-Taylor instability simulations.