how can i get a lower vector-field energy?

after reconstructing a field from the velocities, dyn.vf.VectorField(adata, basis='umap', M=1000, pot_curl_div=True, min_vel_corr=0.8, restart_num=500, cores=20 )

I observed a poor energy-decreasing pattern.

Are there any parameters I can tune to improve this energy behavior?

[chansigit]

Thank you, I found the “Effects of parameters in vector field reconstruction” part in your paper.

I tried these parameters: M, lambda_ and set MaxIter 5k~10k, and found:

1. the velocity streamline on the cells did not vary a lot, and the cell potential landscape did not vary a lot either;
2. the streamlines outside of the cells varied when I set different M and lambda_. Higher lambda_ values made the streamline stubborn and straight as expected (under-fitting). Lower M values increase the sparsity of the streamlines as expected;
3. the streamlines outside of the cells are not very stable- the arrow directions and the whirlpool locations changed a lot and.
4. the parameter tuning rarely improved energy descent curve, and the energy keeps oscillating up and down.

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Let me ask some additional questions:

1. the convergence of the vector-field energy seems poor in my case, can I tune some optimization hyper-parameters like step length or learning rate to attenuate the oscillation?
2. some points on the energy curve once reaches the lowest historical local minima and soon went back to higher levels, can I resume the energy to the local minima step after finishing the optimization?
3. since I have no idea what cell types the streamline-possessing empty area represent, plus the streamline outside of the cells are unstable, how should i interpret these part of the vector field?

Thank you again for your kind help

[Xiaojieqiu]

regarding the energy profile, generally it should decrease dramatically from the first iteration. My experience is that often times the default parameters works well and if you want more refined vector field, try increase basis function M to 500 or 100 as well as increase the maximal learning iterations, maxIter