Mohamad H. Kazma Mohamad H. Kazma

Software

Below are the companion codes for some of my publications. Each repository allows one to reproduce the numerical experiments in the corresponding paper. I hope that the codes help verify and extend the results presented in the respective publications.

Variational observability Gramian (Var-Gram) for nonlinear systems, with the empirical Gramian and Lyapunov-exponent computations. The Var-Gram is demonstrated on combustion reaction networks (H2O2, 9 species; GRI-Mech 3.0, 53 species) modeled through Cantera, with implicit Runge–Kutta integration of the variational dynamics and a greedy sensor placement formulation that selects which species to measure.

Paper: Observability for Nonlinear Systems: Connecting Variational Dynamics, Lyapunov Exponents, and Empirical Gramians. IEEE Transactions on Control of Network Systems, 2026. DOI · arXiv

Coupled overland flow and Green–Ampt infiltration represented as a differential-algebraic state-space model, with distribution-agnostic uncertainty propagation of uncertain paramerters to watershed states under partial gauging. Includes BDF and semi-implicit DAE solvers, Monte Carlo validation via Latin hypercube sampling, and the two test catchments from the paper: a synthetic V-tilted domain and the USDA-ARS Walnut Gulch experimental watershed.

Paper: Exploring Uncertainty Propagation in Coupled Hydrologic and Hydrodynamic Systems via Distribution-Agnostic State Space Analysis. Advances in Water Resources, 2026. DOI · arXiv

MSWQ-SP

Python

Multi-species water quality sensor placement for drinking water distribution networks. Constructs state-averaged observability measures that retain submodularity across varying hydraulic and water quality scenarios, and selects sensor locations with a greedy algorithm while retaining provable approximation guarantees. Includes validation on EPANET benchmark networks.

Paper: Observability and Generalized Sensor Placement for Nonlinear Quality Models in Drinking Water Networks. Journal of Water Process Engineering, 2025. DOI · arXiv