Predictive modeling of aircraft dynamics

A system configured to predict behavior of a number of aircraft, wherein the system comprises:

• a sensor, on an aircraft that comprises a sensor cone, configured to sense parameters that indicate a current state, respectively, for each of a number of aircraft in the sensor cone;
• a controller;
• 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:
• train a neural network;
• apply, for any numerical conditioning challenges encountered during training, a loss function only to a residual output, excluding a skip connection;
• subsequently input a current state, respectively, of the number of aircraft in the sensor cone into a number of hidden layers of the neural network, wherein the neural network is fully connected and comprises a feedforward skip connection from the current state of the number of aircraft in the sensor cone to all outputs of the neural network;
• input an action applied to the aircraft that comprises the sensor into the hidden layers concurrently with the current state;
• determine, by the hidden layers according to the current state and current action, a residual output that comprises an incremental difference in the current state of the aircraft that comprises the sensor resulting from the current action;
• feed forward, by the skip connection, the current state of the aircraft that comprises the sensor to all outputs of the neural network;
• add the residual output to the current state to determine a next state of the aircraft that comprises the sensor, wherein the next state defines a trajectory for the aircraft that comprises the sensor for a next timestep;
• and
• control, based upon the next state, a next action of the aircraft that comprises the sensor with the controller.

A computer program product configured to predict behavior of a number of aircraft, wherein the computer program product comprises a controller configured to determine a next action of an aircraft that comprises a sensor, wherein the sensor comprises a sensor cone, and a computer-readable storage medium that comprises program instructions configured to:

• receive, from the sensor, parameters that indicate a current state, respectively, for each of the number of aircraft in the sensor cone;
• train a neural network;
• apply, for any numerical conditioning challenges encountered during training, a loss function only to a residual output, excluding a skip connection;
• subsequently input the current state of the number of aircraft in the sensor cone into a number of hidden layers of the neural network, wherein the neural network is fully connected and comprises a feedforward skip connection from the current state of the number of aircraft in the sensor cone to all outputs of the neural network;
• input an action applied to the aircraft that comprises the sensor into the hidden layers concurrently with the current state;
• determine, by the hidden layers according to the current state and current action, a residual output that comprises an incremental difference in a state of the aircraft that comprises the sensor resultant from the current action;
• feed forward, by a skip connection, the current state of the aircraft that comprises the sensor to all outputs of the neural network;
• add the residual output to the current state to determine a next state of the aircraft that comprises the sensor, wherein the next state defines a trajectory for the aircraft for a next timestep;
• and
• control, with the controller and based upon the next state, a next action of the aircraft that comprises the sensor.