Inference of hidden variables in systems of differential equations with genetic programming
 Theodore W. Cornforth,
 Hod Lipson
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The datadriven modeling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in realworld settings, it is typically the case that one or more variables are unmeasured or “hidden.” Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverseengineer both the form and parameters of ODE models with hidden variables.
 Title
 Inference of hidden variables in systems of differential equations with genetic programming
 Journal

Genetic Programming and Evolvable Machines
Volume 14, Issue 2 , pp 155190
 Cover Date
 201306
 DOI
 10.1007/s1071001291754
 Print ISSN
 13892576
 Online ISSN
 15737632
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Genetic programming
 Ordinary differential equations
 Hidden variables
 Modeling
 Symbolic identification
 Authors

 Theodore W. Cornforth ^{(1)}
 Hod Lipson ^{(2)}
 Author Affiliations

 1. Biological Statistics and Computational Biology, Cornell University, 1198 Comstock Hall, Ithaca, NY, 148532601, USA
 2. Mechanical and Aerospace Engineering, Cornell University, 239 Upson Hall, Ithaca, NY, 148537501, USA