- #1
jamie.j1989
- 79
- 0
Hi, I want to try out a bit of machine learning or deep learning with an optimisation problem in the lab. However, I'm confused at what the best option would even be or whether my optimisation problem is even applicable to either.
Firstly, the lab set up hasn't been built yet, I am computing the outcome. The model consists of an analytical solution to the magnetic field produced by current loops separated by some distance along the same axis. These current loops produce a quadrupole field where atoms for the proceeding experiment can be trapped at the zero-field region. However, first, the atoms must be transported down the common axis of the loops by ramping varying currents through these loops. Which amounts to moving the magnetic field-zero along the same path. There are various constraints along the way, such as the maximum currents in the loops the minimum field gradient at the zero-field and the time taken.
Does this sound like a problem that can be optimized with either machine learning or deep learning? I'd like to get into it if so as there are many experimental sequences that could be neatly optimised.
Thanks
Firstly, the lab set up hasn't been built yet, I am computing the outcome. The model consists of an analytical solution to the magnetic field produced by current loops separated by some distance along the same axis. These current loops produce a quadrupole field where atoms for the proceeding experiment can be trapped at the zero-field region. However, first, the atoms must be transported down the common axis of the loops by ramping varying currents through these loops. Which amounts to moving the magnetic field-zero along the same path. There are various constraints along the way, such as the maximum currents in the loops the minimum field gradient at the zero-field and the time taken.
Does this sound like a problem that can be optimized with either machine learning or deep learning? I'd like to get into it if so as there are many experimental sequences that could be neatly optimised.
Thanks
Last edited by a moderator: