My interests lie at the intersection of nonlinear dynamics, stochastic analysis and mathematical modeling. In my graduate studies, I worked extensively on functional differential equations, studying growth rates and blow-up of solutions in deterministic and stochastic systems with memory. More recently, I have focused on applying mathematical methods to build and analyze models in applications and I have ongoing interdisciplinary collaborations with colleagues in ecology and developmental biology. Mathematically, my applied projects involve studying mean-field limits of interacting particle systems, pattern formation in PDEs and IDEs, and spatial dynamics in heterogeneous media. You can read more about my research interests and plans here.
Mathematical models of embryonic development
Working closely with experimental collaborators, we developed a novel mathematical model to explain the unexpected formation of ectopic cortical regions in the brains of mutant mice during early development.
J. Feng*, W. H. Hsu*, D. D. Patterson, C. S. Tseng, Z. H. Zhuang, H. W. Hsin, Y.T. Huang, A. Faedo, J. L. Rubenstein, J. D. Touboul and S.J. Chou, COUP-TFI specifies the medial entorhinal cortex identity and induces differential cell adhesion to determine the integrity of its boundary with neocortex, Science Advances, Vol. 7, No. 27 (2021), eabf6808. [*equal contribution]
Spatial vegetation models in mathematical ecology
A thorough understanding of the dynamics and stability of tropical biomes such as rainforest and savanna is crucial to conservation efforts. I am presently working on rigorously deriving and analyzing spatially extended versions of classical models in this domain – our models aim to more realistically reflect vegetation dynamics by accounting for the spatial structure and environmental heterogeneity observed empirically.
L. Xu, D. D. Patterson, A. C. Staver, S. A. Levin, J. Wang, Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach, Proceedings of the National Academy of Sciences, Vol. 118, No. 24 (2021), e2103779118.
D. D. Patterson, A. C. Staver, S. A. Levin, J. D. Touboul, Probabilistic foundations of spatial mean-field models in ecology and applications, SIAM Journal on Applied Dynamical Systems, Vol. 19, No. 4 (2020), 2682-2719.
Finite-time blow-up of systems with memory
There is a rich literature on blow-up criteria for nonlinear Volterra equations and also a considerable array of results regarding the estimation of the blow-up time. However, the problem of determining the behavior of solutions near blow-up is relatively open and this is the most novel aspect of my work in this area.
J. A. D. Appleby and D. D. Patterson, Blow-up and superexponential growth in superlinear Volterra equations, Discrete and Continuous Dynamical Systems (Series A), Vol. 38, No. 8 (2018), 3993-4017.
J. A. D. Appleby and D. D. Patterson, Growth rates of solutions of superlinear ordinary differential equations, Applied Mathematics Letters, Vol. 71 (2017), 30-37.
Discrete systems with memory
Volterra summation equations are general discrete-time models for processes whose evolution depends on their entire history (e.g. time series models in economics and finance). If solutions become unbounded it is natural to rescale them, but does this process preserve economically relevant properties? We answer this question for linear equations with both random and deterministic forcing sequences.
J. A. D. Appleby and D. D. Patterson, Large Fluctuations and growth rates of linear Volterra summation equations, Journal of Difference Equations and Applications, Vol. 23, No. 6 (2017), 1047-1080.
Growth rates of systems with memory
The evolution of many phenomena depends not only on their present state but also on their past states. In continuous time, incorporating this dependence leads to the study of functional differential equations (FDEs). These papers investigate how the memory of past states affects the growth rate of such systems, and how these results are impacted by both random and deterministic forcing.
Simulations illustrating the sharpness of almost sure asymptotic bounds on solutions for nonlinear stochastic FDEs developed in the works below (see Appleby and Patterson, AMC (2021) for details).
J. A. D. Appleby and D. D. Patterson, Growth and fluctuation in perturbed nonlinear Volterra equations, Applied Mathematics and Computation, Vol. 396, (2021) 125938.
J. A. D. Appleby and D. D. Patterson, Growth rates of sublinear functional and Volterra differential equations, SIAM Journal on Mathematical Analysis, Vol. 50, No. 2 (2018), 2086-2110.
J. A. D. Appleby and D. D. Patterson, Memory dependent growth in sublinear Volterra differential equations, Journal of Integral Equations and Applications, Vol. 29, No. 4 (2017), 531-584.
J. A. D. Appleby and D. D. Patterson, Hartman-Wintner growth results for sublinear functional differential equations, Electronic Journal of Differential Equations, Vol. 2017, No. 21 (2017), 1-45.
Convergence rates of stable solutions
How quickly do solutions to a nonlinear ordinary differential equation with a globally stable equilibrium decay? These papers consider this question for scalar ordinary and stochastic differential equations. The use of the theory of regularly varying functions is a recent theme in QTDE and we employ regular variation here to prove very precise asymptotic results.
J. A. D. Appleby and D. D. Patterson, Classification of convergence rates of solutions of perturbed ordinary differential equations with regularly varying nonlinearity, Electron. J. Qual. Theory Differ. Equ., Proc. 10th Coll. Qualitative Theory of Diff. Equ., No. 3 (2016), 1-38.