Predictive modeling of aircraft dynamics

A system for training an encoder, the system comprising:

• a storage device configured to store program instructions;
• and one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• input a current state of a number of aircraft into a recurrent layer of a neural network, wherein the current state comprises
• trajectory data and heading angle data, and wherein the current state comprises a reduced state in which a value of a specified parameter is missing;
• input an action applied to the aircraft into the recurrent layer concurrently with the current state;
• learn, by the recurrent layer, a value for the parameter missing from current state;
• input an output of the recurrent layer into a number of fully connected hidden layers;
• determine, by the hidden layers according to the current state, learned value, and current action, a residual output that comprises an incremental difference from one timestep to the next timestep in the state of the aircraft resulting from the current action, wherein
• the current state is an accumulation of residual outputs from at least one hundred timesteps;
• input the residual output into a loss function;
• input a number of ground truth residual values into the loss function concurrently with the residual output;
• input an output from the loss function into a gradient descent optimizer that computes gradients for the recurrent layer and hidden layers;
• and
• update connection weights in the recurrent layer and hidden layers according to the computed gradients.

A computer program product for training an encoder, the computer program product comprising:

• a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
• inputting a current state of a number of aircraft into a recurrent layer of a neural network, wherein the current state comprises
• trajectory data and heading angle data, and wherein the current state comprises a reduced state in which a value of a specified parameter is missing;
• inputting an action applied to the aircraft into the recurrent layer concurrently with the current state;
• learning, by the recurrent layer, a value for the parameter missing from current state;
• inputting an output of the recurrent layer into a number of fully connected hidden layers;
• determining, by the hidden layers according to the current state, learned value, and current action, a residual output that comprises an incremental difference from one timestep to the next timestep in the state of the aircraft resulting from the current action, wherein
• the current state is an accumulation of residual outputs from at least one hundred timesteps;
• inputting the residual output into a loss function;
• inputting a number of ground truth residual values into the loss function concurrently with the residual output;
• inputting an output from the loss function into a gradient descent optimizer that computes gradients for the recurrent layer and hidden layers;
• and
• updating connection weights in the recurrent layer and hidden layers according to the computed gradients.