Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials
Year: 2026
Type: article
Abstract: ABSTRACT Conventional metamaterial inverse design is hindered by stochastic generative models that lack physical grounding and often fail to reach optimal performance. Here, we introduce a framework coupling a physics‐informed neural operator (DeepONet) and a 3D geometry generator (3D‐cVAE) with a Directional Latent Hybridization (DLH) strategy. By merging dominant traits from parent geometries in the latent space, our approach enables deterministic latent optimization, yielding a high prediction accuracy for energy ( R 2 = 0.957) and force ( R 2 = 0.981). Unlike random noise‐based generation, which suffers from a 0% success rate at high volume fractions ( V r = 0.8), the DLH strategy maintains a 73% success rate and achieves significantly lower volume errors (< 4.81%). Experimental validation using additively manufactured thermoplastic polyurethane (TPU) lattices confirms that DLH‐optimized architectures exceed the performance of their base designs, achieving up to 24.03 J of absorbed energy and a peak force of 9.80 kN. This framework establishes a novel physics‐informed generative paradigm for discovering next‐generation metamaterials with physically interpretable and predictable performance.
Source: Advanced Materials Technologies
DOI: 10.1002/admt.71199
Authors: Semin Ahn, Jun Young Choi, Sung‐Hoon Ahn
Institution: Seoul National University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,680
OpenAlex ID: W7170133141