Exploring the efficacy of neural networks for trajectory compression and the inverse problem
This work addresses trajectory compression and inverse problems in physics simulations, but it is incremental as it applies existing neural network methods to a specific domain.
The paper tackled the problem of predicting initial conditions for nonlinear trajectories using neural networks, achieving sub-meter deviation accuracy for target points within a 2-kilometer radius.
In this document, a neural network is employed in order to estimate the solution of the initial value problem in the context of non linear trajectories. Such trajectories can be subject to gravity, thrust, drag, centrifugal force, temperature, ambient air density and pressure. First, we generate a grid of trajectory points given a specified uniform density as a design parameter and then we investigate the performance of a neural network in a compression and inverse problem task: the network is trained to predict the initial conditions of the dynamics model we used in the simulation, given a target point in space. We investigate this as a regression task, with error propagation in consideration. For target points, up to a radius of 2 kilometers, the model is able to accurately predict the initial conditions of the trajectories, with sub-meter deviation. This simulation-based training process and novel real-world evaluation method is capable of computing trajectories of arbitrary dimensions.