首页|Machine learning sparse reaction-diffusion models from stochasticdynamics and s patiotemporal patterns
Machine learning sparse reaction-diffusion models from stochasticdynamics and s patiotemporal patterns
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“In recent years, live-cell imaging has generated detailed spatiotemporal datase ts of biochemical networkswithin cells. These networks often exhibit characteri stics of spatially-distributed excitable systems,with propagating waves of sign aling activity that govern processes such as cell migration, division, andother essential physiological functions. Traditionally, these reaction-diffusion syst ems have been modeledusing stochastic partial differential equations incorporat ing spatial Langevin-type dynamics. Although these knowledge-based models have p rovided valuable insights, they are typically not directly inferredfrom experim ental data.
NETL
NSTL
