NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:
Initial Amendment Date:	August 16, 1993
Latest Amendment Date:	September 9, 1997
Award Number:	9308639
Award Instrument:	Standard Grant
Program Manager:	S. Kamal Abdali CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	July 15, 1993
End Date:	June 30, 1998 (Estimated)
Total Intended Award Amount:	$97,953.00
Total Awarded Amount to Date:	$97,953.00
Funds Obligated to Date:	FY 1993 = $92,953.00 FY 1995 = $5,000.00
History of Investigator:	Feng Zhao (Principal Investigator) fz@alum.mit.edu
Recipient Sponsored Research Office:	Ohio State University Research Foundation -DO NOT USE 1960 KENNY RD Columbus OH  US  43210-1016 (614)688-8734
Sponsor Congressional District:	03
Primary Place of Performance:	Ohio State University 1960 KENNY RD COLUMBUS OH  US  43210-1016
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	QR7NH79713E5
Parent UEI:
NSF Program(s):	NUMERIC, SYMBOLIC & GEO COMPUT, CISE Research Resources, ARTIFICIAL INTELL & COGNIT SCI
Primary Program Source:	app-0193  app-0195
Program Reference Code(s):	2865, 9218, 9251, 9264, HPCC
Program Element Code(s):	286500, 289000, 685600
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Computationally simulating complex physical systems in engineering design has become a common practice. Yet most of today's simulations rely on extensive numerical computations. Nonlinear physical systems can exhibit extremely complex behaviors that defy human analysis and pure numerical simulations. The analysis and design for these systems are limited by the available computational power and the system complexities. This research project will develop powerful computer simulation technologies for the analysis and design of nonlinear control systems. Programmable primitives will be developed for constructing simulations and synthesizing high-performance controllers. Hybrid computation will be for integrating symbolic and numerical methods with AI reasoning and representation techniques in (1) manipulating and visualizing the dynamics of physical systems based on a phase-space representation, and (2) guiding the execution of numerical computations. The expected results of this is to expand the scope of what current numerical simulation programs or state-of-the-art knowledge-based systems can do solving pressing scientific and engineering problems.

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