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【学术报道】Data Science Plus Dynamical Systems: What Can We Learn ?

YINGNANJINGSHIFANDAXUESHUXUEKEXUEXUEYUANYAOQING,MEIGUOYILINUOYILIGONGXUEYUANJIAOSHOUDUANJINQIAOJIAOSHOUYU2020NIAN6YUE27RISHANGWUZAIyaboDENGLUSHUKEYUANJINXINGXIANSHANGXUESHUJIAOLIUBINGZAIXINGJIANLOU505HUIYISHIZUOTIMUWEI“Data Science Plus Dynamical Systems: What Can We Learn ?”DEXUESHUBAOGAO,SHUXUEKEXUEXUEYUANGAOHONGJUNJIAOSHOUZHUCHILEBENCIBAOGAOHUI,SHUKEYUANBUFENJIAOSHI、YINGYONGSHUXUEBUFENSHUOSHI、BOSHIYANJIUSHENGCANJIALEBENCIBAOGAOHUI。

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段教授首先指出用随机动力系统的理论考虑Date Science是合理的。然后段教授对Date Science用了两种方法处理。第一种是用偏概率的方法。首先把Drift diffusion & jump measure通过概率密度表示,然后用推广的Nonlocal Kramers-Moyal formulas得到用sample path data表示的Drift diffusion & jump measure。另一种方法是用泛函分析的方法处理,构建方程的无穷维生成子同样能够解决问题。最后段教授对这两种方法得到的结果都进行了数值模拟,从中可以直观的看到动力学的有关特征。

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  • 更新时间

    2020NIAN06YUE30RI 10:45

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