DEDiscover

DEDiscover is a workflow-based differential equation modeling software tool for scientists, statisticians, and modelers. Whether you need to do quick simulation, develop sophisticated models, or teach mathematical concepts, DEDiscover combines a powerful computation engine with a user-friendly interface to give you a tool that’s better, faster, and easier-to-use. (Source: http://freecode.com/)


References in zbMATH (referenced in 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Clairon, Quentin: A regularization method for the parameter estimation problem in ordinary differential equations via discrete optimal control theory (2021)
  2. Moore, James R.; Ahmed, Hasan; Manicassamy, Balaji; Garcia-Sastre, Adolfo; Handel, Andreas; Antia, Rustom: Varying inoculum dose to assess the roles of the immune response and target cell depletion by the pathogen in control of acute viral infections (2020)
  3. Clairon, Quentin; Brunel, Nicolas J.-B.: Tracking for parameter and state estimation in possibly misspecified partially observed linear ordinary differential equations (2019)
  4. Clairon, Quentin; Brunel, Nicolas J.-B.: Optimal control and additive perturbations help in estimating ill-posed and uncertain dynamical systems (2018)
  5. Maderazo, Dominic L.; Flegg, Jennifer A.; Neeland, Melanie R.; de Veer, Michael J.; Flegg, Mark B.: Physiological factors leading to a successful vaccination: a computational approach (2018)
  6. Moore, James; Ahmed, Hasan; Jia, Jonathan; Akondy, Rama; Ahmed, Rafi; Antia, Rustom: What controls the acute viral infection following yellow fever vaccination? (2018)
  7. Yan, Ada W. C.; Cao, Pengxing; Heffernan, Jane M.; McVernon, Jodie; Quinn, Kylie M.; La Gruta, Nicole L.; Laurie, Karen L.; McCaw, James M.: Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host (2017)
  8. Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A.: Mathematical models for immunology: current state of the art and future research directions (2016)
  9. Giles Hooker and James Ramsay and Luo Xiao: CollocInfer: Collocation Inference in Differential Equation Models (2016) not zbMATH
  10. Yan, Ada W. C.; Cao, Pengxing; McCaw, James M.: On the extinction probability in models of within-host infection: the role of latency and immunity (2016)
  11. Crauste, F.; Terry, E.; Le Mercier, I.; Mafille, J.; Djebali, S.; Andrieu, T.; Mercier, B.; Kaneko, G.; Arpin, C.; Marvel, J.; Gandrillon, O.: Predicting pathogen-specific CD8 T cell immune responses from a modeling approach (2015)
  12. Petrie, Stephen M.; Butler, Jeff; Barr, Ian G.; McVernon, Jodie; Hurt, Aeron C.; McCaw, James M.: Quantifying relative within-host replication fitness in influenza virus competition experiments (2015)
  13. Murillo, Lisa N.; Murillo, Michael S.; Perelson, Alan S.: Towards multiscale modeling of influenza infection (2013)
  14. Lee, Yeonok; Wu, Hulin: Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection (2012)
  15. Louzoun, Yoram; Ganusov, Vitaly V.: Evolution of viral life-cycle in response to cytotoxic T lymphocyte-mediated immunity (2012)
  16. Dobrovolny, Hana M.; Gieschke, Ronald; Davies, Brian E.; Jumbe, Nelson L.; Beauchemin, Catherine A. A.: Neuraminidase inhibitors for treatment of human and avian strain influenza: a comparative modeling study (2011)