A learned interface-aware neural Newton preconditioner improves convergence on difficult CZM increments while preserving the original discrete solution set and force-displacement response.
Neural-initialized newton: Accelerating nonlinear finite elements via operator learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Low-L2-error neural operators for PDE warm-starts can yield indefinite Jacobians; energy-penalized fine-tuning restores positive definiteness and achieves up to 5.4x speedup on 6.4M DOF hyperelasticity problems.
A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.
citing papers explorer
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Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations
A learned interface-aware neural Newton preconditioner improves convergence on difficult CZM increments while preserving the original discrete solution set and force-displacement response.
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Tackling multiphysics problems via finite element-guided physics-informed operator learning
A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.