What is your goal here? If your evaluation metric doesn’t care about the form of F, i.e. only cares about how well your predictions do, this is just standard supervised learning. However, given the way you phrased it, it sounds like there are some geometric or structural properties of your problem that allow you to synthesize training data. Just note that, if you can exploit structural properties of your problem, it may be much faster using standard nonlinear 2nd order optimizers. Will depend on what you are trying to do, though..
lustiz t1_isza6yn wrote
Reply to [D] Solving energy minimization problems using neural networks by joeggeli
What is your goal here? If your evaluation metric doesn’t care about the form of F, i.e. only cares about how well your predictions do, this is just standard supervised learning. However, given the way you phrased it, it sounds like there are some geometric or structural properties of your problem that allow you to synthesize training data. Just note that, if you can exploit structural properties of your problem, it may be much faster using standard nonlinear 2nd order optimizers. Will depend on what you are trying to do, though..