Model Structure Identification and the Growth of Knowledge

  • M. B. Beck
  • Z. Lin
  • J. D. Stigter


This chapter honors Peter, then, in recounting my career-long experience (1970–2010) of staring down the devilishly difficult: the problem of model structure identification—of using models for discovery. I still regard this matter as one of the grand challenges of environmental modeling (Beck et al., White Paper, 2009). If I appear modest about our progress in the presence of such enormity, so I am. But let no-one presume that I am therefore not greatly enthused by the progress I believe I and my students (now colleagues) have made over these four decades. It has been a privilege to be allowed the time to work on such a most attractive and engaging topic.


Extended Kalman Filter Aquaculture Pond Scientific Visualization Visual Metaphor Model Identifiability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Support for this work has been provided over the decades by the University of Cambridge, the International Institute for Applied Systems Analysis, Imperial College London, and the University of Georgia (UGA). In particular, funding for the Environmental Process Control Laboratory of UGA, together with support for graduate assistantships for ZL and JDS, has come from the Wheatley-Georgia Research Alliance endowed Chair in Water Quality and Environmental Systems. The freedom of enquiry enabled through this form of financial support has simply been invaluable. We are also indebted to J P Bond, for his visualization and graphic design of Figs. 4.6, 4.7 and 4.8.


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.University of GeorgiaAthensUSA
  2. 2.North Dakota State UniversityFargoUSA
  3. 3.Wageningen UniversityWageningenThe Netherlands

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