2020/07/29 by Humphrey Yang, Kuanren Qian, Yang, Humphrey +13 · 2 citations
Engineering · #Advanced Materials and Mechanics #D.2.2 #FOS: Computer and information sciences #H.5.2 #Human-Computer Interaction (cs.HC) #Innovations in Concrete and Construction Materials #J.6 #Modular Robots and Swarm Intelligence
paper · pdf · doi:10.48550/arxiv.2007.15065
openalex publication_date 2020/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Morphing materials allow us to create new modalities of interaction and fabrication by leveraging dynamic behaviors of materials. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.