A research team from Purdue University’s Department of Computer Science and Institute for Digital Forestry, with collaborator Sören Pirk at Kiel University in Germany, has discovered that artificial intelligence can simulate tree growth and shape.
The DNA molecule encodes both tree shape and environmental response in one tiny, subcellular package. In work inspired by DNA, Bedrich Benes, professor of computer science, and his associates developed novel AI models that compress the information required for encoding tree form into a megabyte-sized neural model.
After training, the AI models encode the local development of trees that can be used to generate complex tree models of several gigabytes of detailed geometry as an output.
In two papers, one published in ACM Transactions on Graphics and the other in IEEE Transactions on Visualizations and Computer Graphics, Benes and his co-authors describe how they created their tree-simulation AI models.
“The AI models learn from large data sets to mimic the intrinsic discovered behavior,” Benes said.
Non-AI-based digital tree models are quite complicated, involving simulation algorithms that consider many mutually affecting nonlinear factors. Such models are needed in endeavors such as architecture and urban planning, as well as in the gaming and entertainment industries, to make designs more realistically appealing to their potential clients and audiences.
After working with AI models for nearly 10 years, Benes expected them to be able to significantly improve the existing methods for digital tree twins. The size of the models was surprising, however. “It’s complex behavior, but it has been compressed to rather a small amount of data,” he said.
Co-authors of the ACM Transactions on Graphics paper were Jae Joong Lee and Bosheng Li, Purdue graduate students in computer science. Co-authors of the IEEE Transactions on Visualization and Computer Graphics paper were Li and Xiaochen Zhou, also a Purdue graduate student in computer science; Songlin Fei, the Dean’s Chair in Remote Sensing and director of the Institute for Digital Forestry; and Sören Pirk of Kiel University, Germany.
The researchers used deep learning, a branch of machine learning within AI, to generate growth models for maple, oak, pine, walnut and other tree species, both with and without leaves. Deep learning involves developing software that trains AI models to perform specified tasks through linked neural networks that attempt to mimic certain functionalities of the human brain.
“Although AI has become seemingly pervasive, thus far it has mostly proved highly successful in modeling 3D geometries unrelated to nature,” Benes said. These include endeavors related to computer-aided design and improving algorithms for digital manufacturing.
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