The application discloses a
cell migration behavior prediction method based on
physical information width learning, which utilizes
cell scratch experiment data to construct a
base function matrix, combines a Fisher-KPP reaction
diffusion equation to construct a nonlinear least square problem containing physical mechanism constraints, and adopts an enhanced nonlinear least square disturbance
algorithm to iteratively solve output weights, so as to realize prediction of
cell density space-
time evolution. The application converts traditional deep network iterative training into a nonlinear least square problem, greatly reduces a search space through
physical information initialization and
linearization approximation, and realizes order-of-magnitude improvement of training speed. The application constructs a width
learning architecture containing feature nodes and enhanced nodes, effectively avoids the gradient vanishing problem in
deep learning, combines analytical derivative calculation, eliminates cumulative error of
automatic differentiation on high-order derivatives, and can more accurately capture space-
time evolution characteristics in
cell migration.