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

arXiv: https://arxiv.org/abs/2606.09266 · HTML: https://mlsanity.com/paper/2606.09266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 60 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers](https://mlsanity.com/paper/1805.08154/) (2018)
- [On Sampling Top-K Recommendation Evaluation](https://mlsanity.com/paper/2106.10621/) (2021)
- [GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance](https://mlsanity.com/paper/2305.12073/) (2023)
- [Multifunctional Metasurface Design with a Generative Adversarial Network](https://mlsanity.com/paper/1908.04851/) (2019)
- [Topology optimization of acoustic metasurfaces by using a two-scale homogenization method](https://mlsanity.com/paper/2010.10844/) (2020)
- [A Portable and Stealthy Inaudible Voice Attack Based on Acoustic Metamaterials](https://mlsanity.com/paper/2501.15031/) (2025)
- [MetaGuardian: Enhancing Voice Assistant Security through Advanced Acoustic Metamaterials](https://mlsanity.com/paper/2508.09728/) (2025)

## Abstract (arXiv)

Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that matches the desired response at one frequency may deviate at others, and modifying geometry to improve one sub-band often perturbs neighboring sub-bands. Yet existing broadband inverse-design approaches are either constrained by predefined templates, or rely on image representations that fail to preserve the geometric precision and structural connectivity required by acoustic structures. We present MetaSeq, a physics-guided, sequence-based generative framework for acoustic metamaterial inverse design. At its core, MetaSeq introduces a language that represents each AMM as a structured sequence, rather than as a pixel grid or fixed template. This representation preserves precise geometry, explicitly encodes connectivity, and casts inverse design as a sequence-to-sequence task from target response to structure sequence. MetaSeq further constructs a balanced, high-fidelity dataset with efficient calibration and complexity-based sampling. To address the one-to-many nature of inverse design, MetaSeq combines supervised pretraining with reinforcement learning fine-tuning guided by a physics-based solver and validity checker. Extensive evaluations against COMSOL and five baselines show that MetaSeq reduces response error by 45% over the best baseline.