This directory contains files for the prototype study for stochasticity conducted by Suresh Kumar of ACFR and Ole Nielsen of GA.



Project outline:

The case study is based on a validation conducted against a wavetank experiment emulating the 1993 Okushiri Island tsunami. 


1)Investigate independent, identically distributed errors in bathymetry. Decide a standard deviation of the bathymetry errors.


The important files are:
  project.py: Controls number of samples etc
  create_realisatons.py: Makes a set of perturbed bathymetries
  run_model.py: Run the simulation for each realisation and output three timeseries for each 
		


 
(2)Establish that the Monte-Carlo estimates have converged

    Brute force validation where we run say 1000 simulations and then
    2000, and evaluate whether the mean solution at the 3 monitoring
    stations has significantly changed.

 

(3)Measures of performance at the three channels where measurements 
are available 

    (a) 1 standard deviation from the mean (time averaged)

    (b) Windowed RMS error measure

    (c) Raw RMS error

    (d)Visual inspection

 
Milestone: Evaluate the effects of the stochasticity of bathymetry on
	   the computed solution using the measures of performance
	   listed here to decide on continuance of this line of work.

 

Second mini-project (Pending success of earlier project):

Introduce spatially correlated errors in bathymetry and perform
Monte-Carlo simulations with sparse, locally correlated deviates.


Research Questions:
 
* Which resolution is necessary?
* What is the effect of noise in the friction terms?
* Effect of Boundary conditions, source terms and initial condition.
* Can the whole process be done stochastically - 
      perhaps a hybrid where CFD kick starts a stochastic 
      predictive process (like ITO)