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Tim Sauer

Professor of Mathematics

Department of Mathematics
George Mason University
Mail Stop 3F2
Fairfax, VA 22030
 Office:
Email:
Phone:
Fax: 		4209 Exploratory Hall
tsauer (at) gmu.edu
+1 (703) 993-1471
+1 (703) 993-1491
Photo of Tim Sauer

Books

Solutions, Code, and Additional Examples for Numerical Analysis, Third Edition.

Selected Publications and Preprints

  1. T. Berry, M. Ferrari, T. Sauer, S. Greybush, D. Ebeigbe, A.J. Whalen, S.J. Schiff, Stabilizing the return to normal behavior in an epidemic. Preprint.
  2. S. Jahedi, T. Sauer, J. Yorke, Robustness of solutions of almost every system of equations. SIAM J. Appl. Math. 82, 1791-1807 (2022).
  3. S. Jahedi, T. Sauer, J. Yorke, Structured systems of nonlinear equations. SIAM J. Appl. Math. 83, 1696-1716 (2023).
  4. S. Schiff, T. Sauer, Towards predictive control of African infant infections. SIAM News. Oct., 2020.
  5. S. Jahedi, T. Sauer, J. Yorke, Robust steady states in ecosystems with symmetries. J. Biol. Dyn. 19, 2259223 (2023).
  6. T. Sauer, T. Berry, D. Ebeigbe, M. Norton, A. Whalen, S. Schiff, Identifiability of infection model parameters early in an epidemic. SIAM J. Cont. Opt. 60, 27-48 (2021)
  7. D. Ebeigbe, T. Berry, S. Schiff, T. Sauer, Poisson Kalman filter for disease surveillance. Phys. Rev. Research 2, 043028 (2020).
  8. A. Rahman, V. Maggioni, X Zhang, PL. Houser, T. Sauer, D. Mocko, Joint assimilation of remotely sensed leaf area index and surface soil moisture into a land surface model. Remote Sensing 14, 437 (2022).
  9. A. Rahman, X. Zhang, Y. Xue, P. Houser, T. Sauer, S. Kumar, D. Mocko, V. Maggioni, A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model. J. Hydrol. X 9, 100063 (2020).
  10. X. Zhang, V. Maggioni, A. Rahman, P. Houser, Y. Xue, T. Sauer, S. Kumar, D. Mocko, The influence of assimilating leaf area index in a land surface model on global water fluxes and storages. Hydrol. Earth Syst. Sci 24, 3775-3788 (2020).
  11. J. Guan, T. Berry, T. Sauer, Limits on reconstruction of dynamical networks | Supplement. Phys. Rev. E 98, 022318 (2018).
  12. T. Berry, T. Sauer, Correlation between system and observation errors in data assimilation. Monthly Weather Review 146, 2913 - 2931 (2018).
  13. F. Hamilton, T. Berry, T. Sauer, Tracking intracellular dynamics through extracellular measurements. PLOS One 13(10):e0205031 (2018).
  14. F. Hamilton, T. Berry, T. Sauer, Correcting observational model error in data assimilation. Chaos 29: 053102 (2019).
  15. F. Hamilton, T. Berry, T. Sauer, Kalman-Takens filtering in the presence of dynamical noise. Eur. Phys. J. Spec. Top. 226, 3239-3250 (2017).
  16. T. Berry, T. Sauer, Consistent manifold representation for topological data analysis. Found. Data Sci. 1, 1-38 (2019).
  17. P. Ssentongo, A.Muwanguzi, U. Eden, T. Sauer, G. Bwanga, G. Kateregga, L. Aribo, M. Ojara, W. Mugerwa, S. Schiff, Changes in Ugandan climate rainfall at the village and forest level. Scientific Reports 8, No. 3351 (2018).
  18. T. Berry, T. Sauer, Density estimation on manifolds with boundary, Comp. Stat. and Data Analysis 107, 1-17 (2017).
  19. F. Hamilton, T. Berry, T. Sauer, Ensemble Kalman filtering without a model. Phys. Rev. X 6, 011021 (2016).
  20. F. Hamilton, T. Berry, T. Sauer, Predicting chaotic time series with a partial model. Phys. Rev. E 92, 010902 (2015).
  21. F. Hamilton, J. Cressman, N. Peixoto, T. Sauer, Reconstructing neural dynamics using data assimilation with multiple models, Europhys. Lett. 107, 68005 (2014).
  22. T. Berry, T. Sauer, Local kernels and the geometric structure of data. J. Applied and Comp. Harmonic Analysis 40, 439-469 (2016).
  23. A. Whalen, S. Brennan, T. Sauer, S. Schiff, Observability and controllability of nonlinear networks: The role of symmetry, Phys. Rev. X 5, 011005 (2015).
  24. S. Schiff, S. Ranjeva, T. Sauer, B. Warf, Rainfall drives hydrocephalus in East Africa, J. Neurosurg.: Pediatrics 10, 161-167 (2012).
  25. T. Berry, R. Cressman, Z. Greguric Ferencek, T. Sauer, Time-scale separation from diffusion-mapped delay coordinates. SIAM J. Appl. Dyn. Sys. 12, 618-649 (2013).
  26. T. Sauer, Computational solution of stochastic differential equations. Wiley Interdiscip. Rev. Comput. Stat. 5, 362-371 (2013).
  27. F. Hamilton, T. Berry, N. Peixoto, T. Sauer, Real-time tracking of neuronal network structure using data assimilation. Phys. Rev. E 88, 052715 (2013)
  28. T. Berry, F. Hamilton, N. Peixoto, T. Sauer, Detecting connectivity changes in neuronal networks. J. Neurosci. Meth. 209, 388-397 (2012).
  29. T. Berry, T. Sauer, Adaptive ensemble Kalman filtering of nonlinear systems. Tellus A 65, 20331 (2013).
  30. A. Mitra, A. Manitius, T. Sauer, Prediction of single neuron spiking activity using an optimized nonlinear dynamic model. IEEE Eng. Med. Biol. Soc. 2012, 2543-6 (2012).
  31. D. Napoletani, M. Signore, T. Sauer, L. Liotta, E. Petricoin, Homologous control of protein signaling networks. J. Theo. Biol. 279, 29-43 (2011).
  32. T. Sauer, S. Schiff, Data assimilation for heterogeneous networks: the consensus set. Phys. Rev. E 79, 051909 (2009).
  33. T. Sauer, Observing periodically forced systems of difference equations. Journal of Difference Equations and Applications 16, 269-273 (2010).
  34. T. Sauer, Numerical solution of stochastic differential equations in finance. Handbook of Computational Finance, pp. 529-550. Eds. J.-C. Duan, W. Hardle, J. Gentle. Springer, Berlin-Heidelberg (2012).
  35. T. Sauer, Global convergence of max-type equations. Journal of Difference Equations and Applications 17, 1-8 (2011).
  36. T. Sauer, Convergence of rank-type equations. Applied Mathematics and Computation 217, 4540-7 (2011).
  37. T. Berry, T. Sauer, Convergence of periodically-forced rank-type equations. Journal of Difference Equations and Applications 18, 417-429 (2012).
  38. D. Napoletani, T. Sauer, D. Struppa, E. Petricoin, L. Liotta, Augmented sparse representation of protein signaling networks. J. Theo. Biol. 255, 40-52 (2008).
  39. D. Napoletani, T. Sauer, Reconstructing the topology of sparsely-connected dynamical networks. Phys. Rev. E 77, 026103 (2008).
  40. S. Schiff, T. Sauer, Kalman filter control of a model of spatiotemporal cortical dynamics. J. Neural Eng. 5, 1-8 (2008).
  41. T. Sauer, Detection of periodic driving in nonautonomous difference equations. Advanced Studies in Pure Mathematics 53, 301-309 (2009).
  42. D. Napoletani, D. Struppa, T. Sauer, V. Morozov, N. Vsevelodov, C. Bailey, Functional dissipation microarrays for classification. Pattern Recognition 40, 3393-3400 (2007).
  43. T. Sauer, Attractor reconstruction. Scholarpedia 1(10):1727 (2006).
  44. D. Napoletani, C. Berenstein, T. Sauer, D. Struppa, D. Walnut, Delay coordinate embeddings as a data mining tool for denoising speech signals. Chaos 16, 043116 (2006).
  45. T. Sauer, Computer arithmetic and sensitivity of natural measure. Journal of Difference Equations and Applications 11, 669-676 (2005).
  46. S. Weinstein, T. Sauer, R. Kumar, S. Schiff, Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures. Neuroimage 28, 1043 - 1055 (2005).
  47. K. Jerger, S. Weinstein, T. Sauer, S. Schiff, Multivariate linear discrimination of seizures. Clinical Neurophysiology 116, 545-551 (2005).
  48. T. Sauer, Reconstruction of shared nonlinear dynamics in a network. Physical Review Letters 93, 198701-4 (2004).
  49. B. Hunt, E. Kalnay, E. Kostelich, E. Ott, D.J. Patil, T. Sauer, I. Szunyogh, J. Yorke, A. Zimin, Four-dimensional ensemble Kalman filtering. Tellus A56, 273-277 (2004).
  50. T. Sauer, Chaotic itinerancy based on attractors of one-dimensional maps. Chaos 13, 947-952 (2003).
  51. T. Sauer, Shadowing breakdown and large errors in dynamical simulations of physical systems. Phys. Rev. E 65, 036220 (2002).