Kyle T. Mandli

Assistant Professor

Columbia University in the City of New York
Applied Physics and Applied Mathematics Department
288 Engineering Terrace
Mail Code: 4701
New York, NY 10027
Phone: (212) 854-4485
Fax: (212) 854-8257
Office: 288 Engineering Terrace

Research Interests

My research is primarily concerned with how to apply finite volume methods, adaptive mesh refinement, and other computational science approaches to a variety of geophysical flow problems, including storm surges and tsunamis. These flows all have shallow water characteristics, which allow us to apply the same general methods to many different flows. My research specifically revolves around two main ideas: the first is to adapt current models so they can easily be solved in a depth averaged context, and the second is to implement robust and efficient solvers for the simulation of these flows. Additionally, I work to ensure that the solvers are accessible to the people who need them, e.g. debris flow modelers, field geologists, and others who are responsible for hazard preparation and response. Consequently, I strive to adhere to good software development practices, such as literate programming, and to design frameworks that are easy to extend and maintain.

CV and Resume

Peer Reviewed Publications

Fractally homogeneous, air-sea turbulence with Frequency-integrated, wind-driven gravity waves
Colton J. Conroy, Kyle T. Mandli, Ethan J. Kubatko.
In prep (2017).
hp discontinuous Galerkin methods for parametric, wind- driven water wave models
Colton J. Conroy, Ethan J. Kubatko, A. Nappi, R. Sebian, D. West, Kyle T. Mandli.
Submitted to Advances in Water Resources (2017).
Dynamically adaptive data-driven simulation of extreme hydrological flows
Pushkar K. Jain, Kyle T. Mandli, Ibrahim Hoteit, Omar I. Knio, Clint N. Dawson.
Ocean Modelling, Volume 122, 2018, Pages 85-103. .
Quantifying uncertainties in Fault Slip Distribution During the Tohoku Tsunami using Polynomial Chaos
Ihab Sraj, Kyle T. Mandli, Omar M. Knio, Clint N. Dawson, and Ibrahim Hoteit.
Ocean Dynamics (2017) 67: 1535.
Hybrid Analog-Digital Solution of Nonlinear Partial Differential Equations
Huang, Y., Guo, N., Seok, M., Tsividis, Y., Mandli, K. T., Sethumadhavan, S.
MICRO-50, October 14-18, 2017, Cambridge, MA, USA.
Baysian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate.
Giraldi, L., Le Maître, O. P, Mandli, K.T., Dawson, C.N., Hoteit, I., Knio, O.M.
Comput Geosci 21, 683-699 (2017).
Clawpack: building an open source ecosystem for solving hyperbolic PDEs
Kyle T. Mandli, Aron J. Ahmadia, Marsha J. Berger, Donna A. Calhoun, David L. George, Yiannis Hadjimichael, David I. Ketcheson, Gray I. Lemoine, and Randall J. LeVeque.
PeerJ Comput. Sci. 2, e68 (2016).
Visualizing Uncertainties in a Storm Surge Ensemble Data Assimilation and Forecasting System
Thomas Höllt, M. Umer Altaf, Kyle T. Mandli, Markus Hadwiger, Clint N. Dawson, and Ibrahim Hoteit.
Natural Hazards 1-20 (2015).
Uncertainty Quantification and Inference of Manning's Friction Coefficient using DART Buoy Data during the Tohoku Tsunami
Ihab Sraj, Kyle T. Mandli, Omar M. Knio, Clint N. Dawson, and Ibrahim Hoteit.
Ocean Modelling, Volume 83, Pages 82-97 (2014). arxiv
Adaptive Mesh Refinement for Storm Surge
Kyle T. Mandli and Clint N. Dawson.
Ocean Modelling, Volume 75, March 2014, Pages 36-50. arxiv
ForesetClaw: Hybrid forest-of-octrees AMR for hyperbolic conservation laws.
Carsten Burstedde, Donna Calhoun, Kyle Mandli, and Andy R. Terrel.
Proceedings of ParCo 2013, September 10-13, 2013, Technical University of Munich, Munich, Germany. arxiv
A Numerical Method for the Two Layer Shallow Water Equations
Kyle T. Mandli.
Ocean Modeling, Volume 72, December 2013, Pages 80-91. arxiv.
ManyClaw: Slicing and dicing Riemann solvers for next generation highly parallel architectures
A. R. Terrel and K. T. Mandli.
TACC-Intel Symposium on Highly Parallel Architectures, 2012.
PyClaw: Accessible, Extensible, Scalable Tools for Wave Propagation Problems
David I. Ketcheson, Kyle T. Mandli, Aron J. Ahmadia, Amal Alghamdi, Manuel Quezada de Luna, Matteo Parsani, Matthew G. Knepley, and Matthew Emmet,
SIAM J. Sci. Comput., 34(4), 210-231, (2012). arxiv
The GeoClaw software for depth-averaged flows with adaptive refinement
M. J. Berger, D. L. George, R. J. LeVeque, and K. T. Mandli.
Advancement in Water Resources, Volume 34, Issue 9, Pages 1195-1206, September 2011. arxiv
Finite Volume Methods for the Multilayer Shallow Water Equations with Applications to Storm Surges
K.T. Mandli, Ph.D. Thesis, July 2011.
PetClaw: A Scalable Parallel Nonlinear Wave Propagation Solver for Python
Amal Alghamdi, Aron Ahmadia, David I. Ketcheson, Matthew G. Knepley, Kyle T. Mandli and Lisandro Dalcin.
2011 Spring Simulation Multi-Conference, SpringSim'11, Boston, MA, USA, April 03-07, 2011. Volume 6: Proceedings fo the 19th High Performance Computing Symposium (HPC); 01/2011.

Links to Code Projects

Conservation Laws Package
Python-based solver based on Clawpack that includes bridges to multiple other packages including PETSc and SharpClaw
Research into exploitation of intra-node parallelism for hyperbolic PDE solvers via Clawpack like interfaces.
GitHub Clawpack Project Pages