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Yanyan He, Ph.D.

Affiliations: 
2013 Mathematics Florida State University, Tallahassee, FL, United States 
Area:
Mathematics
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"Yanyan He"
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M Yousuff Hussaini grad student 2013 Florida State
 (Uncertainty quantification and data fusion based on Dempster-Shafer theory.)

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Publications

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Waldrop LD, He Y, Hedrick TL, et al. (2020) Functional morphology of gliding flight I. Modeling reveals distinct performance landscapes based on soaring strategies. Integrative and Comparative Biology
Bhaduri A, He Y, Shields MD, et al. (2018) Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis Journal of Computational Physics. 371: 732-750
He Y, Razi M, Forestiere C, et al. (2018) Uncertainty quantification guided robust design for nanoparticles’ morphology Computer Methods in Applied Mechanics and Engineering. 336: 578-593
He Y, Hussaini MY, Gong YU, et al. (2016) Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis. Journal of Applied Clinical Medical Physics / American College of Medical Physics. 17: 5737
Chen X, He Y, Xiu D. (2015) An efficient method for uncertainty propagation using fuzzy sets Siam Journal On Scientific Computing. 37: A2488-A2507
Wang C, Qiu Z, He Y. (2015) Fuzzy stochastic finite element method for the hybrid uncertain temperature field prediction International Journal of Heat and Mass Transfer. 91: 512-519
He Y, Mirzargar M, Kirby RM. (2015) Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory International Journal of Approximate Reasoning. 66: 1-15
Wang C, Qiu Z, He Y. (2015) Fuzzy interval perturbation method for uncertain heat conduction problem with interval and fuzzy parameters International Journal For Numerical Methods in Engineering. 104: 330-346
Chen W, Cui Y, He Y, et al. (2012) Application of Dempster-Shafer theory in dose response outcome analysis. Physics in Medicine and Biology. 57: 5575-85
He Y, Yousuff Hussaini M, Ma J, et al. (2012) A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data Pattern Recognition. 45: 3463-3471
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