Skip to Main Content
 

Global Search Box

 
 
 
 

ETD Abstract Container

Abstract Header

Data Driven Learning of Dynamical Systems Using Neural Networks

Mussmann, Thomas Frederick

Abstract Details

2021, Master of Mathematical Sciences, Ohio State University, Mathematical Sciences.
We review general numerical approaches for discovering governing equations through data driven equation recovery. That is when the equations governing a dynamical system is unknown and depends on some hidden subset of variables. We review the structure of Neural Networks, Residual Neural Networks, and Recurrent Neural Networks. We also discuss the Mori Zwanzig formulation using history to substitute for hidden variables. We explore two examples, first is modeling Neuron Bursting with hidden variables using a Neural Network. Second, we examine particle traffic models and select one, which we dimensionally reduce and then attempt to predict future state from this dimensional reduction.
Dongbin Xiu (Advisor)
Dustin Mixon (Committee Member)
53 p.

Recommended Citations

Citations

  • Mussmann, T. F. (2021). Data Driven Learning of Dynamical Systems Using Neural Networks [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618589877977348

    APA Style (7th edition)

  • Mussmann, Thomas. Data Driven Learning of Dynamical Systems Using Neural Networks. 2021. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1618589877977348.

    MLA Style (8th edition)

  • Mussmann, Thomas. "Data Driven Learning of Dynamical Systems Using Neural Networks." Master's thesis, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618589877977348

    Chicago Manual of Style (17th edition)