simulation

I have been doing object-oriented programming for a few years now, and I have not done much functional programming. I have an interest in flight simulators, and am curious about the functional programming aspect of Lisp. Flight simulators or any other real world simulator makes sense to me in an object-oriented paradigm. Here are my questions: Is object oriented the best way to represent a real world simulation domain? I know that Common Lisp has CLOS (OO for lisp), but my question is really about writing a flight simulator in a functional language. So if you were going to write it in Lisp,

I'm working on a project in which parallel computing would be a huge advantage. The project simulates multiple Simulink models. I did the simulation with a normal for-Loop, but since it takes days to simulate I decided to try the "parfor"-Loop . But that's where the problem begins. First I'll give you pictures of my code, the workspace and the Simulink-part which is causing me problems: Here's my code: apool = gcp('nocreate'); if isempty(apool) apool = parpool('local'); end wpath = pwd; parfor k = 1:number_of_models load_system(strcat(wpath,'\Models_Folder\',House(k).model_name)); set_param

First off, I'm not entirely sure if this is the correct place to be posting this, as perhaps it should go in a more statistics-focussed forum. However, as I'm planning to implement this with R, I figured it would be best to post it here. Please apologise if I'm wrong. So, what I'm trying to do is the following. I want to simulate data for a total of 250.000 observations, assigning a continuous (non-integer) value in line with a kernel density estimate derived from empirical data (discrete), with original values ranging from -5 to +5. Here's a plot of the distribution I want to use. It's quite

In a Python script I'm writing I am simulating multivariate normal random vectors with the expression np.random.multivariate_normal(np.zeros(dim_obs), y_cov) My script runs, but generates the following warning: RuntimeWarning: covariance is not positive-semidefinite. Also the little debug print statements I throw in there print False most of the time print( np.all(np.linalg.eigvals(y_cov) > 0) ) Why is this throwing false positives? My y_cov is positive semi-definite because it is (sorry about the lack of TeX markup) B x x'B' + y y' where the B is a matrix, and the others are random vectors

I want to create a multi-agent simulation model for a real word manufacturing process to evaluate some dispatching rules. The simulation needs to produce event logs to evaluate time effect of the dispatching rules compared to the real manufacturing event logs. How can I incorporate the 'current simulation time' into this kind of multi-agent, message passing intensive simulation? Background: The classical discrete event simulation (which handles the time-advancement nicely) cannot be applied here, as the agents in the system represent relatively complex behavior and routing requirements plus