Adil Siripatana

PhD Student

Research Interests

​Adil is interested in uncertainty quantification and data assimilation for coastal ocean forecasting. His Ph.D. thesis focuses on developing uncertainty reduction and parameter estimation techniques for coastal ocean model, using Bayesian inference and Spectral methods such as Ensemble Kalman Filter (EnKF), Markov Chain Monte Carlo (MCMC), and Polynomial Chaos (PC) expansion.

Selected Publications

Ensemble Kalman filter inference of spatially-varying Manning's n coffiecients in the coastal ocean
A. Siripatana, T. Mayo, O. Knio, C. Dawson, O. Le Maitre, I. Hoteit
Journal of Hydrology, 562, 664-684, 2018

Assessing an ensemble Kalman filter inference of Manning's n coefficient of an idealized tidal inlet against a polynomial chaos based MCMC
A. Siripatana, T. Mayo, I. Sraj, O. Knio, C. Dawson, O. Le Maitre, I. Hoteit
Ocean Dynamics, 67 (8), 2017

Single-site Lennard-Jones models via polynomial chaos of Monte Carlo molecular simulation
A. Kadoura, A. Siripatana, S. Sun, O. M. Knio, I. Hoteit
The Journal of Chemical Physics, 144 (21), 2016


  • M.Sc., Earth Science, KAUST, Thuwal, Saudi Arabia, 2014
  • B.Sc., Computational Science, Walailak University, Thailand, 201