Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design

Researchers introduce MetaSeq, a physics-guided generative framework that uses sequence-based representations to design acoustic metamaterials with broadband responses. The approach reduces design errors by 45% compared to existing methods by combining machine learning with physics-based validation, addressing a long-standing challenge in materials engineering where structures optimized for one frequency often fail at others.

MetaSeq represents a meaningful advancement in computational materials design by tackling the acoustic metamaterial inverse design problem, where engineers must discover structures matching desired acoustic properties across multiple frequencies simultaneously. Traditional approaches struggle because acoustic dispersion causes frequency-dependent behavior—optimizing a structure for one frequency often degrades performance at adjacent frequencies. The framework's innovation lies in its sequence-based representation rather than pixel grids or templates, enabling precise geometric encoding while maintaining structural connectivity that image-based methods lose. This technical contribution bridges machine learning and physics by combining supervised pretraining with reinforcement learning guided by validated physics solvers. The 45% error reduction versus baselines suggests meaningful practical impact for industries relying on acoustic control, including noise mitigation, ultrasonic applications, and vibration engineering. The physics-guided validation mechanism ensures generated designs remain physically realizable, addressing a critical gap in purely neural approaches. The balanced, complexity-sampled dataset construction demonstrates thoughtful experimental design. From a broader perspective, this exemplifies how domain-specific languages and physics-aware machine learning can solve engineering inverse problems that resist generic deep learning approaches. The methodology provides a template for similar structural design challenges across materials science. However, real-world deployment requires validation at manufacturing scales and cost constraints not addressed in the paper. The research demonstrates that sequence models, traditionally dominant in language processing, effectively encode geometric and topological information for physical systems.

• →MetaSeq uses sequence-based representations instead of image grids to preserve geometric precision in acoustic metamaterial design
• →Physics-guided reinforcement learning combined with validity checking reduces design response error by 45% over baseline methods
• →The framework addresses broadband inverse design by handling acoustic dispersion across multiple frequencies simultaneously
• →Sequence-to-sequence modeling proves effective for structural inverse design problems beyond traditional template or pixel-based approaches
• →The approach demonstrates how domain-specific languages can encode connectivity and topology for physics-guided machine learning