STOCHASTIC COMPUTATIONAL MODEL PARAMETER SYNTHESIS SYSTEM

• US 20130124164A1
• Filed: 11/09/2012
• Published: 05/16/2013
• Est. Priority Date: 11/11/2011
• Status: Active Grant

• Current Assignee: Carnegie Mellon University, University of Central Florida Research Foundation Inc. (State University System of Florida)
• Original Assignee: Carnegie Mellon University, University of Central Florida Research Foundation Inc. (State University System of Florida)
• Inventors: Jha, Sumit K., Langmead, Christopher J.
• Granted Patent: US 9,558,300 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]

Abstract

A stochastic computational model parameter synthesis system comprising at least one processor capable of executing processor executable code, and a non-transitory computer memory operably coupled with the at least one processor and storing processor executable code, which when executed by processor, causes processor to synthesize at least one parameter of a stochastic computational model to satisfy one or more behavioral specifications of properties observed in a modeled system. The processor generates and searches randomized projections of a first parameter space having n dimensions into one or more second abstract parameter space having d dimensions, where d is less than n, and outputs a signal indicative of a synthesized parameter value to the user.

Claims

1. A stochastic computational model parameter synthesis system comprising at least one processor capable of executing processor executable code, and a non-transitory computer memory operably coupled with the at least one processor and storing processor executable code, which when executed by the at least one processor, causes the at least one processor to synthesize at least one parameter of a stochastic computational model to satisfy one or more behavioral specifications of properties observed in a modeled system by:

receiving a first input signal from a user over a network interface, the first input signal indicative of a parametric stochastic computational model to be carried out by the at least one processor;

accessing the at least one parameter to be synthesized;

receiving a second input signal indicative of a behavioral specification indicative of a property observed in the modeled system;

receiving a minimum probability with which the stochastic model must satisfy the behavioral specification;

generating one or more randomized projections of a first parameter space having n dimensions into one or more second abstract parameter space having d dimensions, where d is less than n, wherein a distance between n+1 chosen anchor points in the first parameters space and a point corresponding to the parameter values with the highest probability of satisfying the specification in the one or more second abstract parameter space is preserved;

searching the one or more second abstract parameter space by exhaustively searching discretized values in the one or more second abstract parameter space obtained by performing a randomized projection of the first parameter space having n dimensions; and

transmitting an output signal to a user, the output signal indicative of at least one synthesized parameter value.

2. The system of claim 1, wherein the randomized projection includes choosing n+1 linearly independent anchor points in the first parameter space.

3. The system of claim 2, wherein distances of discretized abstract parameter values from projections of n+1 anchor points are used to identify candidate parameter values in the first parameter space.

4. The system of claim 1, wherein randomized metric embeddings matrices are used to construct the one or more abstract parameter space.

5. The system of claim 1, wherein randomized projections preserve the distance between the n+1 linearly independent anchor points and the parameter value of the stochastic model that satisfies the given specification with the highest probability.

6. The system of claim 1, wherein the randomized projections are generated by two or more parallel computer processors.

7. The system of claim 1, wherein the modeled system is a physical system.

8. The system of claim 7, wherein the modeled system is a biological system.

9. The system of claim 8, wherein the biological system is described using an agent-based model.

10. The system of claim 9, wherein the at least one parameter is a kinetic parameter of the biological system.

11. The system of claim 1, wherein the modeled system is a non-physical system.

12. The system of claim 11, wherein the modeled system is a financial trading system.

13. The system of claim 12, wherein the at least one parameter is a risk of at least one asset in an investment portfolio.

14. The system of claim 1, wherein the stochastic model is selected from a continuous time Markov chain, a discrete time Markov chain, a stochastic differential equation, and a jump diffusion process.

15. The system of claim 1, wherein the property specification is an adapted finitely monitorable specification.