| 1 | This directory contains files for the prototype study for stochasticity conducted by Suresh Kumar of ACFR and Ole Nielsen of GA. |
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| 5 | Project outline: |
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| 6 | |
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| 7 | The case study is based on a validation conducted against a wavetank experiment emulating the 1993 Okushiri Island tsunami. |
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| 9 | |
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| 10 | 1)Investigate independent, identically distributed errors in bathymetry. Decide a standard deviation of the bathymetry errors. |
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| 12 | |
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| 13 | The important files are: |
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| 14 | project.py: Controls number of samples etc |
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| 15 | create_realisatons.py: Makes a set of perturbed bathymetries |
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| 16 | run_model.py: Run the simulation for each realisation and output three timeseries for each |
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| 20 | |
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| 21 | (2)Establish that the Monte-Carlo estimates have converged |
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| 22 | |
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| 23 | Brute force validation where we run say 1000 simulations and then |
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| 24 | 2000, and evaluate whether the mean solution at the 3 monitoring |
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| 25 | stations has significantly changed. |
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| 28 | |
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| 29 | (3)Measures of performance at the three channels where measurements |
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| 30 | are available |
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| 31 | |
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| 32 | (a) 1 standard deviation from the mean (time averaged) |
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| 33 | |
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| 34 | (b) Windowed RMS error measure |
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| 35 | |
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| 36 | (c) Raw RMS error |
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| 37 | |
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| 38 | (d)Visual inspection |
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| 39 | |
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| 40 | |
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| 41 | Milestone: Evaluate the effects of the stochasticity of bathymetry on |
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| 42 | the computed solution using the measures of performance |
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| 43 | listed here to decide on continuance of this line of work. |
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| 46 | |
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| 47 | Second mini-project (Pending success of earlier project): |
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| 48 | |
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| 49 | Introduce spatially correlated errors in bathymetry and perform |
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| 50 | Monte-Carlo simulations with sparse, locally correlated deviates. |
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| 51 | |
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| 52 | |
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| 53 | Research Questions: |
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| 54 | |
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| 55 | * Which resolution is necessary? |
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| 56 | * What is the effect of noise in the friction terms? |
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| 57 | * Effect of Boundary conditions, source terms and initial condition. |
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| 58 | * Can the whole process be done stochastically - |
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| 59 | perhaps a hybrid where CFD kick starts a stochastic |
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| 60 | predictive process (like ITO) |
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