  To incorporate prior knowledge as well as measurement uncertainties in the
traditional long short term memory (LSTM) neural networks, an efficient sparse
Bayesian training algorithm is introduced to the network architecture. The
proposed scheme automatically determines relevant neural connections and adapts
accordingly, in contrast to the classical LSTM solution. Due to its
flexibility, the new LSTM scheme is less prone to overfitting, and hence can
approximate time dependent solutions by use of a smaller data set. On a
structural nonlinear finite element application we show that the
self-regulating framework does not require prior knowledge of a suitable
network architecture and size, while ensuring satisfying accuracy at reasonable
computational cost.
