MAS-PNCG accelerates IPC by incrementally updating multilevel MAS preconditioners via Sparse-Input Woodbury, adding Hessian-aware 2D subspace minimization and per-subdomain CCD, achieving up to 5.66x speedup over Newton-PCG baselines.
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A homogenization scheme derives the nonlinear stress resultants and stiffnesses of periodic rod metamaterials by solving the microstructural unit with uniform macroscale strain and helically periodic boundary conditions using special Cosserat rod theory.
Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.
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An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact
MAS-PNCG accelerates IPC by incrementally updating multilevel MAS preconditioners via Sparse-Input Woodbury, adding Hessian-aware 2D subspace minimization and per-subdomain CCD, achieving up to 5.66x speedup over Newton-PCG baselines.
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Homogenization of rod-like metamaterials as a special Cosserat rod
A homogenization scheme derives the nonlinear stress resultants and stiffnesses of periodic rod metamaterials by solving the microstructural unit with uniform macroscale strain and helically periodic boundary conditions using special Cosserat rod theory.
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Neural Control: Adjoint Learning Through Equilibrium Constraints
Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.