![]() ![]() Our approach retains the essential PBD feature of stable behavior with constrained computational budgets, but also allows for convergent behavior with expanded budgets. We show that a position-based, rather than constraint-based nonlinear Gauss-Seidel approach solves these problems. ![]() Recent works have shown that PBD can be related to the Gauss-Seidel approximation of a Lagrange multiplier formulation of backward Euler time stepping, where each constraint is solved/projected independently of the others in an iterative fashion. We show that ignoring the effects of neighboring constraints limits its convergence and stability properties. Furthermore, PBD projects one constraint at a time. ![]() This is particularly relevant since the efficient creation of large data sets of plausible, but not necessarily accurate elastic equilibria is of increasing importance with the emergence of quasistatic neural networks. Even though PBD is based on the projection of static constraints, PBD is best suited for dynamic simulations. We develop a novel approach to address problems with PBD for quasistatic hyperelastic materials. ![]() Its primary strength is its robustness when run with limited computational budget. Position based dynamics is a powerful technique for simulating a variety of materials. ![]()
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