Jonathan Christopher Mattingly
- James B. Duke Distinguished Professor
- Professor of Mathematics
Research Areas and Keywords
Stochastic Analysis, Malliavin Calculus, Ergodic Theory
Stochastic and Random PDEs, Stochastic Dynamical Systems, Mathematical Ecology and Evolution, Metabolic and Cellular modeling, Out of equilibrium statistical mechanics
Markov Chain Mixing, Stochastic Numerical Methods, High Dimensional Random Algorithms
PDE & Dynamical Systems
Stochastic and Random PDEs, Stochastic Dynamical Systems, Malliavin Calculus, Fluid Mechanics, Approximating invariant measures
Stochastic and Random PDEs, Stochastic Dynamical Systems, Fluid Mechanics
Stochastic and Random PDEs, Stochastic Dynamical Systems, Stochastic Analysis, Malliavin Calculus, Markov Chain Mixing, Ergodic Theory, High Dimensional Random Algorithms, Probability on stratified spaces, Out of equilibrium statistical mechanics, Approximating invariant measures
Jonathan Christopher Mattingly grew up in Charlotte, NC where he attended Irwin Ave elementary and Charlotte Country Day. He graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and Computational Mathematics in 1998. After 4 years as a Szego assistant professor at Stanford University and a year as a member of the IAS in Princeton, he moved to Duke in 2003. He is currently a Professor of Mathematics and of Statistical Science.
His expertise is in the longtime behavior of stochastic system including randomly forced fluid dynamics, turbulence, stochastic algorithms used in molecular dynamics and Bayesian sampling, and stochasticity in biochemical networks.
He is the recipient of a Sloan Fellowship and a PECASE CAREER award. He is also a fellow of the IMS and the AMS.
Weinan, E., and J. C. Mattingly. “Ergodicity for the navier-stokes equation with degenerate random forcing: Finite-dimensional approximation.” Communications on Pure and Applied Mathematics, vol. 54, no. 11, Nov. 2001, pp. 1386–402. Scopus, doi:10.1002/cpa.10007. Full Text Open Access Copy
Mattingly, J. C. “Ergodicity of 2D Navier-Stokes equations with random forcing and large viscosity.” Communications in Mathematical Physics, vol. 206, no. 2, Jan. 1999, pp. 273–88. Scopus, doi:10.1007/s002200050706. Full Text
Mattingly, J. C., and Y. G. Sinai. “An elementary proof of the existence and uniqueness theorem for the Navier-Stokes equations.” Communications in Contemporary Mathematics, vol. 1, no. 4, Jan. 1999, pp. 497–516. Scopus, doi:10.1142/S0219199799000183. Full Text Open Access Copy
Holmes, P. J., et al. “Low-dimensional models of coherent structures in turbulence.” Physics Report, vol. 287, no. 4, Jan. 1997, pp. 337–84. Scopus, doi:10.1016/S0370-1573(97)00017-3. Full Text
Herschlag, Gregory, et al. Non-reversible Markov chain Monte Carlo for sampling of districting maps. Open Access Copy
Autry, Eric A., et al. Multi-Scale Merge-Split Markov Chain Monte Carlo for Redistricting. Open Access Copy
Johndrow, James E., and Jonathan C. Mattingly. Error bounds for Approximations of Markov chains used in Bayesian Sampling. Open Access Copy
Leimbach, Matti, et al. Noise-induced strong stabilization.