Systems and methods for building dynamic reduced order physical models

A computer-implemented method comprising:

• obtaining input data and output data of a physical system, the input data representing a dynamic input excitation to the physical system over a period of time, and the output data representing a dynamic output response of the physical system to the dynamic input excitation over the period of time;
• configuring a neural network including an input layer and an output layer, the input layer including a plurality of input units to receive the input data and the output data, and the output layer including one or more output units;
• training the neural network with the input data and the output data of the physical system to predict a dynamic rate of change of the input data via the one or more output units over the period of time, the input layer updated with a free input unit in additional to the plurality of input units, the output layer updated with a free output unit in addition to the one or more output units, the free output unit to predict a rate of change of data received by the free input unit over the period of time;
• and
• generating a simulation object for the physical system, the simulation object including the trained neural network to determine future output data of the physical system in real time.

A non-transitory computer-readable storage medium storing instructions, which when executed by a computer processing system, cause the computer processing system to perform operations comprising:

• obtaining input data and output data of a physical system, the input data representing a dynamic input excitation to the physical system over a period of time, and the output data representing a dynamic output response of the physical system to the dynamic input excitation over the period of time;
• configuring a neural network including an input layer and an output layer, the input layer including a plurality of input units to receive the input data and the output data, and the output layer including one or more output units;
• training the neural network with the input data and the output data of the physical system to predict a dynamic rate of change of the input data via the one or more output units over the period of time, the input layer updated with a free input unit in additional to the plurality of input units, the output layer updated with a free output unit in addition to the one or more output units, the free output unit to predict a rate of change of data received by the free input unit over the period of time;
• and
• generating a simulation object for the physical system, the simulation object including the trained neural network to determine future output data of the physical system in real time.

A system, comprising:

• a memory storing instructions;
• one or more processors coupled to the memory, the one or more processors executing the instructions from the memory, the one or more processors configured to perform operations comprising:
• obtaining input data and output data of a physical system, the input data representing a dynamic input excitation to the physical system over a period of time, and the output data representing a dynamic output response of the physical system to the dynamic input excitation over the period of time,
• configuring a neural network including an input layer and an output layer, the input layer including a plurality of input units to receive the input data and the output data, and the output layer including one or more output units,
• training the neural network with the input data and the output data of the physical system to predict a dynamic rate of change of the input data via the one or more output units over the period of time, the input layer updated with a free input unit in additional to the plurality of input units, the output layer updated with a free output unit in addition to the one or more output units, the free output unit to predict a rate of change of data received by the free input unit over the period of time, and
• generating a simulation object for the physical system, the simulation object including the trained neural network to determine future output data of the physical system in real time.