A probabilistic method for the gradient estimates of some geometric flows Xin Chen Abstract In general, the gradient estimates are very important and necessary for deriving the convergent results in different geometric flows, and most of them are obtained by analytic methods. We will apply a stochastic approach to systematically give gradient estimates for some important geometric quantities under the Ricci flow, the mean curvature flow, the forced mean curvature flow and the Yamabi flow respectively. Our conclusion will give another example that probabilistic tools can be applied to give a simple proof for some problems on geometric analysis. This talk is based on a joint paper with Li-Juan Cheng and Jing Mao.