Simulating the architecture of a termite incipient nest using a convolutional neural network

A pretraining domain decomposition method using artificial neural networks to solve elliptic PDE boundary value problems

Developing methods of domain decomposition (DDM) has been widely studied in the field of numerical computation to estimate solutions of partial differential equations (PDEs). Several case studies have also reported that it is feasible to use the domain decomposition approach for the application of artificial neural networks (ANNs) to solve PDEs. In this study, we devised a pretraining scheme called smoothing with a basis reconstruction process on the structure of ANNs and then implemented the classic concept of DDM. The pretraining process that is engaged at the beginning of the training epochs can make the approximation basis become well-posed on the domain so that the quality of the estimated solution is enhanced. We report that such a well-organized pretraining scheme may affect any NN-based PDE solvers as we can speed up the approximation, improve the solution’s smoothness, and so on. Numerical experiments were performed to verify the effectiveness of the proposed DDM method on ANN for estimating solutions of PDEs. Results revealed that this method could be used as a tool for tasks in general machine learning.

Termite population size estimation based on termite tunnel patterns using a convolutional neural network

Subterranean termites live in large colonies under the ground where they build an elaborate network of tunnels for foraging. In this study, we explored how the termite population size can be estimated using partial information on tunnel patterns. To achieve this, we used an agent-based model to create tunnel patterns that were characterized by three variables: the number of simulated termites (N), passing probability of two termites encountering one another (P), and distance that termites move soil particles (D). To explore whether the N value could be estimated using a partial termite tunnel pattern, we generated four tunnel pattern groups by partially obscuring different areas in an image of a complete tunnel pattern, where: (1) the outer area of the tunnel pattern was obscured (I-pattern); (2) half of the tunnel pattern was obscured (H-pattern); (3) the inner region of the tunnel pattern was obscured (O-pattern); and (4) I- and O-patterns (IO-pattern) were combined. For each group, 80% of the tunnel patterns were used to train a convolutional neural network while the remaining 20% were used for estimating the N value. Estimation results showed that the N estimates for IO-patterns were the most accurate, followed by I-, H-, and O-patterns. This indicates that termite population size can be estimated based on tunnel information near the center of a colony. We briefly discuss the advantages and disadvantages of this method for estimating termite population size.