Dachverband der Österreichsichen Sozialversicherungsräger
In your modell you should have done a single parameter sensitivity analysis as well as a probabilistic sensitivity analysis.The observed data - hopefully - are in the area covered by the probabbilistic sensitivity analysis. If so (and also if not) the sensitivity analyses show you which parameters "move" the results of the model in the direction of the observation. Now you can reevaluate the quality of your parametrization or even of your modelling approach.
If your model and reality are totaly different check the model ;-))
It depends on the questions / field of science you are in. Sometimes scale/granularity of the model has to be adjusted. Sometimes the model misses relevant causal connections.
We know that conducting experiments to get observed data is time-consuming and expensive sometimes. That is one of the reason why you made the model using software to obtain simulated data. In order to ensure the accuracy of the model, you should compare the simulated data (from model) with the observed data (from experiment). If the simulated data is close to the observed data, your model is proved to be accurate and reliable for future use. If it is not, you should check your model again to ensure that all conditions (materials, constraints, loadings, ....) of the model describe the experiment setups exactly. Prof. Harald Kirchsteiger already mentioned this in his comment, I just made it a bit more clear.
After successfully verify the model with the experiment by validating the simulated data with the observed data, now you can confidently use your model to simulate with different model parameters. There are some ways to analyse and develop your idea further:
1. Sensitivity analysis as recommended by prof. Gottfried Endel is worth it to consider. By doing so, you can determine the trend/movement of the obtained data (simulated results). You can also determine the most and the less sensitive parameters to your simulated results.
2. When you plot the experiment data or simulated data with respect to a single input parameter, you possibly have a curve. After that, you can use curve fitting functions in MATLAB or use some available Artificial Neural Network (ANN) packages to predict the explicit mathematical expression of the simulated results (the accurate fitting curve is the curve with lowest errors). This expression/formulation can be used directly for practical designs and further. They are reliable is due to your model and simulated results are already verified before. You should consider MATLAB Curve fitting functions or ANN packages are just a tool to be used.
3. The idea in section 2. right above can be developed further. Now you possibly try to consider multiple input parameters, you possibly have a surface. At this point, some MATLAB surface fitting functions or Polynomial Neural Network (PNN) might be helpful. It might be difficult, but I think that if we can understand clearly the data and can see the data trend by naked eyes, we can do something with it.
The first thing you would want to do is to validate your model with the observed (actual data), herein lies the presentation and analysis of the data. This is done using statistical tools (software). You might want to determine statistical parameters such as chi-square, correlation coefficient, etc. in comparison and validation of your data. If your predicted data highly represents the observed value, then your model is a good fit (well describing the phenomenon).
But when I change the loading condition to tension compression cyclic loading, the curve obtained is very distorted and abstract,1. PNG macro stress-strain curve. 2. PNG is the stress-strain curve of the specified grain drawn by the script of the official website example. The attachment is the official website script used。
This paper presents modelling, simulation and optimization results for a novel SThm probe. The model takes into account thermo-electro-mechanical equations. Moreover, a tip-surface contact model derived by taking into account microscopic multi-asperity contact is proposed and discussed. Results of multi-objective optimization are reported, and fina...
Two-dimensional and three-dimensional tide models were used to simulate the M2 tide in the Gulf of Maine. Model estimates of changes to the tide caused by a tidal barrier in the upper Bay of Fundy were made and compared. Tidal amplitudes in the presence of a barrier increased 30-50 cm for both models. The three-dimensional model uniformly produced...
Résumé. Après avoir situé la contribution des approches multi-agents dans le domaine de la modélisation et de la simulation et cerné son domaine d'applicabilité, nous présentons brièvement ce qui a été réalisé dans le domaine de l'écologie et de la sociologie avant de présenter quelques plate-formes existantes. Nous concluons finalement par quelque...