Jump to: Journal Publications Preprints Theses Invited Talks Contributed Talks
Research Overview
My research is in emerging fast methods for the solution of highdimensional partial differential equations (PDEs) that arise in areas such as mechanics and computer vision. I am particularly interested in static solutions of HamiltonJacobi equations and hyperbolic conservation laws, and developing accessible scientific software for these and other problems. This includes research in numerical methods for partial differential equations, high performance scientific computing, and efficient algorithm development combining traditional discretization techniques and modern machine learning (ML) methods, for highorder solutions to nonlinear problems with steep gradients and shocks.
Current work is in utilizing traditional discretization of PDEs and ML techniques, particularly fixedpoint methods and physicsinformed neural networks (PINNs), for applications in computer vision. This includes object reconstruction through solving inverse problems and object identification through multiobjective methods of reconstruction and surface recognition. Other projects include investigating the use of adaptive mesh refinement (AMR) for efficiently producing highresolution solutions of HamiltonJacobi equations.
Past work has been in highorder accurate methods for hyperbolic conservation laws and highdimensional HamiltonJacobi equations, with a focus on improving efficiency while maintaining accuracy. The above image is an example of the solution to a shape from shading problem, a common problem in computer vision of reconstructing a 3D shape from the shading data of a 2D image by solving a nonlinear HamiltonJacobi equation.
Publications
Refereed Journal
 Z.M. Miksis and Y.T. Zhang, Sparsegrid implementation of fixedpoint fast sweeping WENO schemes for Eikonal equations, Communications on Applied Mathematics and Computation, Accepted (2022). Published online first: https://doi.org/10.1007/s4296702200209x (pdf)
Theses
 Z.M. Miksis, Sparsegrid implementation of fixedpoint fast sweeping WENO schemes for Eikonal equations, PhD dissertation, University of Notre Dame, 2022.
 Z.M. Miksis, An Accelerating Couette Flow in Nek5000: Applications in Oceanography and Magnetohydrodynamics, MS thesis, Illinois Institute of Technology, 2017.
Presentations
Invited Talks
 Numerical solutions to HamiltonJacobi equations on sparse grids and a Stokes problem variant with Nek5000, Virtual presentation, Sandia National Laboratory, Albuquerque, NM, June 2022.
Contributed Talks

Parallel implementation of a sparse grid fast sweeping WENO method for Eikonal equations, Midwest Numerical Analysis Day, University of Michigan, Ann Arbor, MI, May 2022.

A sparse grid fast sweeping WENO method for Eikonal equations, Midwest Numerical Analysis Day, Missouri University of Science and Technology, Rolla, MO, October 2021.

An accelerating Couette flow with MHD in Nek5000, Summer Argonne Student Symposium, Argonne National Laboratory, Lemont, IL, August 2016.

MHD with Nek5000, Nek5000 Users and Developers Meeting, Massachusetts Institute of Technology, Cambridge, MA, August 2016.

Verification of a Stokes problem variant, Graduate Student Colloqium, Illinois Institute of Technology, Chicago, IL, March 2016.