Computational Physics
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Differentiable Finite Element Framework for Learning Constitutive Models via Automatic Differentiation
FE-MAD (Finite Element-Based Material learning via Automatic Differentiation) is a novel end-to-end differentiable framework that integrates constitutive neural network models within a JAX-FEM nonlinear solver. It utilizes automatic differentiation for computing system gradients, eliminating the nee…
MPS Dynamically Compresses Rayleigh-Bénard Turbulence Beyond Static Complexity Bounds
Matrix Product States (MPS) enable dynamical simulations of 2D Rayleigh-Bénard convection up to Ra=10^10, achieving accurate statistical observables like Nusselt number with bond dimension χ comparable to lower Ra=10^9 cases, despite a priori DNS analysis showing non-saturating χ growth to Ra=10^11.…