follicle_experiments.m File Reference

experiments with the model of the human follicle growth More...

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Functions
function res =	follicle_experiments (step, model, model_data, detailed_data)
	experiments with the model of the human follicle growth
function J =	follicleexperiments>my_opt_target (mu, Ytarget, model, reduced_data)

Detailed Description

experiments with the model of the human follicle growth

Definition in file follicle_experiments.m.

Function Documentation

function res = follicle_experiments	(		step,
			model,
			model_data,
			detailed_data
	)

experiments with the model of the human follicle growth

model range is n=200, noutput = 50, nt = 500, time = 19.02 years implicit Euler time-discretization, accept relatively large error at initial time, for large overall time interval and few timesteps and "interactivity"

(step = 1 simulation and visualization of detailed dynamical system, using filecaching => do not call directly) step = 1.1 simulation and visualization of detailed dynamical system => n=20,n_output=20 seems too small step = 1.2 simulation and visualization of detailed dynamical system => n=50, n_output=20 seems better step = 1.3 simulation and visualization of detailed dynamical system => n=50, n_output=50 seems better, no big difference to 1.2 step = 1.4 simulation and visualization of detailed dynamical system => n=70, n_output=50 no big difference to 1.2, 1.3 step = 1.5 simulation and visualization of detailed dynamical system => n=200, n_output=50 seems better takes about 2 minutes. Results saved for step 1.6 parameter variation explicit by setting mu. step 1.6: check influence of different nt on accuracy so OK for me (step = 1.7 load precomputed example for range = 200 show second heap around 70x70 by suitable color scaling. Generate Movie of data => Does not yet work!!!) NEW: step = 1.8, n=200, n_output = 50, initial data distributed over larger domain init_range = 20 and different output functional expectation_M (step = 2 reduced simulation and visualization of approximation => do not call directly) step = 2.1 n=20, n_output = 20, full reduced basis. should be identical to 1.1 (step = 3 detailed and reduced simulation and visualization of error and estimator => do not call directly) step = 3.1 n=20, n_output = 20, full reduced basis. error should be almost 0, but therefore large overestimation (but still small estimator) step = 3.2 n=20, n_output = 20, partial reduced basis (stupid basis) (first 136 states). error should be larger than 0, but overestimation reduced only factor 3 step = 3.3 n=20, n_output = 20, partial reduced basis (lower 136 triangular states). error should be larger than 0, but overestimation reduced only factor 3. Error & estimator less than 3.2, so more clever basis step = 3.4, n=200, n_output = 50, generate POD basis of single trajectory basis is saved for use in further steps and plotted step = 3.41, n=200, n_output = 50, generate POD basis of 9 trajectories basis is saved for use in further steps and plotted step = 3.42, n=200, n_output = 50, generate POD basis of 25 trajectories basis is saved for use in further steps and plotted step = 3.5 experiments with a part of POD-basis from 3.4 step = 3.6 n=200, n_output = 50, generate POD-Greedy basis with true error as indicator, 5x5 p-points logarithmically equidistant distributed search for several mu largest required regularization parameter step = 3.7 test POD-greedy-basis from step 3.6

Parameters

step	step
model	model
model_data	model data
detailed_data	detailed data

Return values

res

res

Required fields of model:

• get_mu —  get mu
• debug —  flag indicating wether debug output shall be turned on
• set_mu —  set mu
• plot_sim_data_state —  plot sim data state
• range —  range
• dim_x —  dim x
• enable_error_estimator —  enable error estimator
• dim_y —  dim y
• nt —  number of time steps for evolution discretizations
• plot_detailed_data —  plot detailed data
• RB_numintervals —  RB numintervals
• mu_ranges —  matrix storing the admissable ranges for parameter vector components. Each two dimensional column vector represents a range for a component specified by model.mu_names.
• A_function_ptr —  A function ptr
• B_function_ptr —  B function ptr
• C_function_ptr —  C function ptr
• D_function_ptr —  D function ptr

Required fields of detailed_data:

• G —  G
• V —  V

Required fields of model_data:

• Ns —  Ns
• Ms —  Ms
• G —  G
• RB —  RB

Definition at line 17 of file follicle_experiments.m.