{"slug": "acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures", "title": "AcoustiPrint: Physics-Guided Neural Design of Manufacturable Acoustic Structures", "summary": "Researchers posted arXiv:2610.30411v1, introducing AcoustiPrint, a physics-informed neural architecture that represents 3D geometries with an implicit neural representation and passes each candidate through a printability operator enforcing layer-wise self-support, discrete material selection and whole-body connectivity before evaluating its acoustic response. AcoustiPrint uses surrogate gradients for the backward pass and a self-supervised printability objective to backpropagate through discrete, non-differentiable fabrication constraints, and the authors demonstrate it for personalized transcranial focused ultrasound, reporting manufacturable acoustic structures with enhanced focal accuracy over density-based and phase-based baselines.", "body_md": "arXiv:2610.30411v1 Announce Type: new \nAbstract: Recent work in additive manufacturing and neural inverse design enables the creation of complex three-dimensional structures to manipulate physical fields, yet these designs must also adhere to discrete, non-differentiable fabrication constraints. Current methods limit the design space by imposing differentiable soft manufacturability loss penalties, or by fixing structures through post-processing, causing a discrepancy between the geometry optimized by the physics model and the final fabricated result. Thus, we present AcoustiPrint, a physics-informed neural architecture for constrained inverse design that represents three-dimensional geometries with an implicit neural representation (INR) and passes each candidate through a printability operator before evaluating its final response. The printability operator executes layer-wise self-support, discrete material selection, and whole-body connectivity, assuring fabrication feasibility. To allow for backpropagation through the discrete non-differentiable operations, we use surrogate gradients for the backward pass and a self-supervised printability objective that progressively drives the INR toward a valid design domain. We demonstrate AcoustiPrint for personalized transcranial focused ultrasound, where inter-patient differences in acoustic properties and head geometry deform wave propagation and requires patient-specific acoustic focusing. AcoustiPrint jointly optimizes three-dimensional geometry and material composition through differentiable wave physics while ensuring manufacturability. Experiments show manufacturable acoustic structures with enhanced focal accuracy and over density-based and phase-based baselines, demonstrating a generalized methodology for neural design through non-differentiable manufacturing constraints and differentiable physics.", "url": "https://wpnews.pro/news/acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures", "canonical_source": "https://arxiv.org/abs/2610.30411", "published_at": "2026-10-23 04:00:00+00:00", "updated_at": "2026-10-23 04:10:57.734702+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks", "artificial-intelligence"], "entities": ["AcoustiPrint", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures", "markdown": "https://wpnews.pro/news/acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures.md", "text": "https://wpnews.pro/news/acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures.txt", "jsonld": "https://wpnews.pro/news/acoustiprint-physics-guided-neural-design-of-manufacturable-acoustic-structures.jsonld"}}