The application discloses a self-supervised
ablation process
grid deformation method and
system based on a graph neural network, and belongs to the technical field of
computational mechanics and numerical
simulation. The method represents a body grid in numerical
simulation of an
ablation thermal protection system as a directed attribute graph, takes grid geometric information and a vertex-by-vertex
ablation coefficient as input features, and constructs a differentiable mapping from an ablation state to a vertex
displacement field. Without any supervised displacement
label, the graph neural network parameters are jointly optimized through three self-supervised constraints of normal
backtracking consistency loss, element effectiveness loss and
displacement field smoothness loss, so that the
grid deformation result meeting the ablation movement requirement is obtained. The application does not need to adjust the
network structure for different element types, can accurately approximate the given ablation
backtracking amount while maintaining the grid element effectiveness, and maintains stability and robustness, thereby providing a self-supervised, differentiable and element type independent implementation mode for dynamic grid movement in ablation numerical
simulation.