  Topology optimization is a critical task in engineering design, where the
goal is to optimally distribute material in a given space for maximum
performance. We introduce Neural Implicit Topology Optimization (NITO), a novel
approach to accelerate topology optimization problems using deep learning. NITO
stands out as one of the first frameworks to offer a resolution-free and
domain-agnostic solution in deep learning-based topology optimization. NITO
synthesizes structures with up to seven times better structural efficiency
compared to SOTA diffusion models and does so in a tenth of the time. In the
NITO framework, we introduce a novel method, the Boundary Point Order-Invariant
MLP (BPOM), to represent boundary conditions in a sparse and domain-agnostic
manner, moving away from expensive simulation-based approaches. Crucially, NITO
circumvents the domain and resolution limitations that restrict Convolutional
Neural Network (CNN) models to a structured domain of fixed size -- limitations
that hinder the widespread adoption of CNNs in engineering applications. This
generalizability allows a single NITO model to train and generate solutions in
countless domains, eliminating the need for numerous domain-specific CNNs and
their extensive datasets. Despite its generalizability, NITO outperforms SOTA
models even in specialized tasks, is an order of magnitude smaller, and is
practically trainable at high resolutions that would be restrictive for CNNs.
This combination of versatility, efficiency, and performance underlines NITO's
potential to transform the landscape of engineering design optimization
problems through implicit fields.
