Timeline for Reaction-diffusion PDE with NDSolve: either very slow or very inaccurate
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Feb 16, 2016 at 11:07 | comment | added | Tamás Czárán | Thank you, Alexei, the AdaptiveMonteCarlo method with the MinRecursion option set sufficiently high above zero seems to do the trick. The speed is not very impressive, but acceptable. I doubt I can expect much more than that with my 2D diffusion problem :-) Thanks again for your help! | |
| Feb 15, 2016 at 16:05 | comment | added | Alexei Boulbitch | @Tamás Czárán Try to look into tutorial/NIntegrateIntegrationStrategies#65285686/Global Adaptive Monte Carlo and Quasi Monte Carlo Strategies. There are some examples including those with spikes. | |
| Feb 15, 2016 at 14:23 | comment | added | Tamás Czárán | Indeed, the single spike of the NS function - which remains spiky all along the simulation - is often missed by the AdaptiveMonteCarlo algorithm - roughly in a fifth of the cases the integral is of zero value which it definitely should not be. (I can't copy code here, unfortunately.) Probably there is a way to increase the MC sampling density, isn't there? | |
| Feb 15, 2016 at 10:50 | comment | added | Tamás Czárán | Thanks for the suggestion - I will try it now with the AdaptiveMonteCarlo algorithm. My worry in the first place (the reason I did not try it) was that the initial conditions for S and NS are very spiky Gaussians, which might not be hit by a random sampling algorithm, but that was just a gut feeling - you might well be right that I should use it instead. | |
| Feb 12, 2016 at 9:18 | history | answered | Alexei Boulbitch | CC BY-SA 3.0 |