Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow

•Weak form physics constraints are incorporated into a fully-connected neural network to predict future responses.•Domain decomposition reduces computational cost and captures local discontinuity.•Our model shows improved accuracy and robustness to noises compared to strong form theory-guided neural networks.