Using bayesopt instead of fmincon in Matlab example of "Solving Burgers PDE with Deep Learning
6 views (last 30 days)
Show older comments
Hello everyone,
I am trying to understand how to solve PDE with PINNs using different optimization methods. in this example (https://www.mathworks.com/help/releases/R2022b/deeplearning/ug/solve-partial-differential-equations-with-lbfgs-method-and-deep-learning.html)
the fmincon and L-BFGS are used to optimize the hyperparameters of the PINN. Actually I am trying to solve this example using the bayesopt but I do not have clue how to replace fmincon.
I hava read the documentation of bayesopt
(https://www.mathworks.com/help/stats/bayesopt.html) and (https://www.mathworks.com/help/stats/optimizablevariable.html)
But what is not clear to me is how to make the hyperparameters of PINNs in this example like neurons, weights and biases to be optimizable variables?
Thanks in advance.
4 Comments
Alan Weiss
on 23 Apr 2023
I don't know how many optimizable variables your model has now. If just a dozen or so then yes, you can use bayesopt, though I am not sure why you would want to. What do you hope to gain? bayesopt is much, much slower than fmincon and cannot use derivative information. Do you suspect that there are multiple local minima? If so, you would almost certainly be better advised to use MultiStart with fmincon instead of bayesopt. But do what you want.
Alan Weiss
MATLAB mathematical toolbox documentation
Answers (0)
See Also
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!