A bilinear scatter encoding plus a masked UNet yields competitive surrogate accuracy and data efficiency on several mesh-based simulation benchmarks, though the encoding is a standard technique.
UCNN: A Convolutional Strategy on Unstructured Mesh
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abstract
In machine learning for fluid mechanics, fully-connected neural network (FNN) only uses the local features for modelling, while the convolutional neural network (CNN) cannot be applied to data on structured/unstructured mesh. In order to overcome the limitations of FNN and CNN, the unstructured convolutional neural network (UCNN) is proposed, which aggregates and effectively exploits the features of neighbour nodes through the weight function. Adjoint vector modelling is taken as the task to study the performance of UCNN. The mapping function from flow-field features to adjoint vector is constructed through efficient parallel implementation on GPU. The modelling capability of UCNN is compared with that of FNN on validation set and in aerodynamic shape optimization at test case. The influence of mesh changing on the modelling capability of UCNN is further studied. The results indicate that UCNN is more accurate in modelling process.
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Learning Mappings in Mesh-based Simulations
A bilinear scatter encoding plus a masked UNet yields competitive surrogate accuracy and data efficiency on several mesh-based simulation benchmarks, though the encoding is a standard technique.