Equivariant neural operators for gradient-consistent topology optimization
David Erzmann,
Sören Dittmer
Abstract:Most traditional methods for solving partial differential equations (PDEs) require the costly solving of large linear systems. Neural Operators (NOs) offer remarkable speed-ups over classical numerical PDE solvers. Here, we conduct the first exploration and comparison of NOs for 3D Topology Optimization. Specifically, we propose replacing the PDE solver within the popular Solid Isotropic Material with Penalization (SIMP) algorithm, which is its main computational bottleneck. For this, the NO not only needs to … Show more
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