丁香园AV

Synopsis

Machine learning allows physicists to make use of large amounts of data for studying physics models and making predictions on system dynamics. In non-equilibrium statistical mechanics, the Fokker-Planck equation (FPE) is used to describe how time-dependent probability density functions (PDFs) evolve in a stochastic system. However, it has been difficult to apply and predict PDF evolution with higher accuracy in real-world systems. Neural networks, which are a subset of machine learning, have been used to perform time series prediction and modelling in many fields. In recent studies, the neural network approach shows physicists a path that more accurately predicts PDF evolution where FPE parameters can be obtained by training on a set of PDF data. This might lead to significant improvement in many real-world applications, such as studies on DNA bubble dynamics in biophysics and heat flux evolution in meteorology. In this talk, I will discuss the neural network approach for modelling a system that is under random fluctuations.