AI-driven design

We integrate the power of data-driven design and optimization, bringing a new level of intelligence to the creative process. By harnessing the vast capabilities of artificial intelligence, we can explore design spaces that were previously unimaginable.

광대역 음향 메타소재 역설계 시스템

(Inverse design framework for broadband acoustic metamaterials)

기존의 음향 소재 설계 방법이 갖는 한계를 극복하기 위해 AI 기반의 새로운 설계 시스템을 개발합니다. 이 기술은 원하는 음향 특성을 가진 복잡한 구조를 자동으로 생성할 수 있으며, 기존 방법보다 훨씬 넓은 주파수 대역에서 뛰어난 차음 효과를 실현합니다. 특히 여러 주파수에서 동시에 최적의 성능을 발휘하는 혁신적인 음향 솔루션을 구현합니다.

데이터 기반 기계적 메타물질 설계

(Data-driven mechanical metamaterial-based design)

진동·열·충격 등 다양한 물리 환경에 대응하는 메타물질을 AI 및 데이터로 통합 설계합니다. 기존 이론 중심 설계의 한계를 극복하고, 다목적 최적화를 통해 더욱 혁신적이고 다양한 구조 설계 솔루션을 제공합니다.

[Related works]

Ji Gyo Park, Baekgyu Kim, Jin Yeong Song, Keon Ko, Ho Kyoung Lee, Dongwhi Choi**, Seunghun Baek**, Sang Min Park**, "Machine learning-driven optimization of locally resonant metamaterials for simultaneous vibration control and effective triboelectric sensing", Nano Energy, 142, Part A, 111224 (2025) (IF = 17.1, Top 5.35% in JCR 2024)
Min Woo Cho*, Seok Hyeon Hwang*, Jun-Young Jang*, Jin Yeong Song, Sun-kwang Hwang, Kyoung Je Cha, Dong Yong Park**, Kyungjun Song**, Sang Min Park**, "Inverse design of non-parameterized ventilated acoustic resonator via variational autoencoder with acoustic response-encoded latent space", Materials Today Communications, 47, 113055 (2025) (IF = 4.5, Top 36.74% in JCR 2024)
Keon Ko*, Min Woo Cho*, Kyungjun Song, Dong Yong Park**, Sang Min Park**, "Inverse design of non-parametric acoustic metamaterials via dual variational autoencoder and iterative transfer learning to break parametric boundaries in dataset", Engineering Applications of Artificial Intelligence, 151, 110735 (2025) (IF = 8.0, Top 2.86% in JCR 2024)
Min Jik Kim, Seon Yeong Yang, Woo Seok Yang, Sehyeok Oh, Sang Min Park** , Da Seul Shin**, "Exploration of physics-related latent vectors in hot working of Inconel 718 superalloy using autoencoder", Journal of Materials Research and Technology, 35, 6749–6762 (2025) (IF = 6.6, Top 10.42% in JCR 2024)
Min Woo Cho, Seok Hyeon Hwang, Jun Young Jang, Sun-kwang Hwang, Kyoung Je Cha, Dong Yong Park**, Kyungjun Song**, Sang Min Park**, "Beyond the limits of parametric design: latent space exploration strategy enabling ultra-broadband acoustic metamaterials", Engineering Applications of Artificial Intelligence, 133, Part F, 108595 (2024) (IF = 7.5, Top 2.79% in JCR 2023)
Min Woo Cho, Keon Ko, Majid Mohammadhosseinzadeh, Ji Hoon Kim, Dong Yong Park, Da Seul Shin**, Sang Min Park**, "Inverse design of Bézier curve-based mechanical metamaterials with programmable negative thermal expansion and negative Poisson’s ratio via data augmented deep autoencoder", Materials Horizons, 11, 2615–2627 (2024) (IF = 12.2, Top 8.90% in JCR 2023)

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