SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows
This work addresses the problem of slow and inaccurate simulations for designers and engineers in HCI and CAD, enabling interactive design workflows for morphing materials, though it is incremental as it builds on existing computational tools.
The paper tackles the bottleneck of lacking effective simulation methods for morphing materials design by introducing SimuLearn, a data-driven method combining finite element analysis and machine learning, achieving real-time simulation at 0.61 seconds with 97% accuracy.
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.