IPS is a general purpose implementation of nonlinear indicial theory, applicable to systems with multiple dependent and independent variables. The IPS formulation is based on characterizing nonlinear systems using a kernel of nonlinear indicial responses (IR) and critical-state responses (CSR). IR/CSR parameterizations can be mixed (i.e., discrete or continuous) and arbitrary-dimensional.
 
The kernel responses of a nonlinear system can be determined analytically, numerically, or experimentally, whichever is available or appropriate. The kernel identification problem amounts to an initial investment, either in terms of experimental data or in terms of simulation results, from which the behavior of the system can then be generalized/predicted at a small computational cost.
 
Once system modeling (choice of inputs and outputs) and problem parameterization are performed by the user, IPS can be used in either prediction mode or extraction mode. The prediction module calculates the system response by integrating that system's indicial (step) and critical-state (bifurcation) responses. The extraction module performs the inverse problem of determining the system's kernel of nonlinear indicial and critical-state responses, based on observed data.
 
IPS has been applied to problems in high angle-of-attack aerodynamics, electronics, and aeroservoelasticity. The software user manual contains a comprehensive description of both the program's options as well as program interfaces with external modules. Use of the Indicial Prediction System is also illustrated through detailed sample cases.
 
The IPS environment offers the following advantages:

	Well-suited for high-fidelity modeling of nonlinear plant characteristics for advanced control.
	Systems approach to modeling unsteady nonlinear processes.
	Solves real-world nonlinear problems; creates mathematical models for complex processes, bypassing lengthy stages of program/code development and debugging.
	Reduces model development time.
	Well-suited for quick development of nonlinear systems as proof-of-concept.
	Easy model adaptability.
	Flexibility to address many applications.