Above are papers related to the following paper: [Paper_ID] Paper #1139195. 	[Title] Program Synthesis and Transformation Techniques for Simpuation, Optimization and Constraint Satisfaction Deductive Synthesis of Numerical [Abstract] Scientists and engineers face recurring problems of constructing, testing and modifying numerical simulation programs. The process of coding and revising such simulators is extremely time-consuming, because they are almost always written in conventional programming languages. Scientists and engineers can therefore benefit from software that facilitates construction of programs for simulating physical systems. Our research adapts the methodology of deductive program synthesis to the problem of constructing numerical simulation codes. We have focused on simulators that can be represented as second order functional programs composed of numerical integration and root extraction routines. We have developed a system that uses first order Horn logic to synthesize numerical simulators built from these components. Our approach is based on two ideas: First, we axiomatize only the relationship between integration and differentiation. We neither attempt nor require a complete axiomatization of mathematical analysis. Second, our system uses a representation in which functions are reified as objects. Function objects are encoded as lambda expressions. Our knowledge base includes an axiomatization of term equality in the lambda calculus. It also includes axioms defining the semantics of numerical integration and root extraction routines. We use depth bounded SLD resolution to construct proofs and synthesize programs. Our system has successfully constructed numerical simulators for computational design of jet engine nozzles and sailing yachts, among others. Our results demonstrate that deductive synthesis techniques can be used to construct numerical simulation programs for realistic applications (Ellman and Murata 1998). Automatic design optimization is highly sensitive to problem formulation. The choice of objective function, constraints and design parameters can dramatically impact the computational cost of optimization and the quality of the resulting design. The best formulation varies from one application to another. A design engineer will usually not know the best formulation in advance. In order to address this problem, we have developed a system that supports interactive formulation, testing and reformulation of design optimization strategies. Our system includes an executable, data-flow language for representing optimization strategies. The language allows an engineer to define multiple stages of optimization, each using different approximations of the objective and constraints or different abstractions of the design space. We have also developed a set of transformations that reformulate strategies represented in our language. The transformations can approximate objective and constraint functions, abstract or reparameterize search spaces, or divide an optimization process into multiple stages. The system is applicable in principle to any design problem that can be expressed in terms of constrained op Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7867. 	[Title] Knowledge-Based Re-engineering of Legacy Programs for Robustness in Automated Design [Abstract] Systems for automated design optimization of complex real-world objects can, in principle, be constructed by combining domain-independent numerical routines with existing domain-specific analysis and simulation programs. Unfortunately, such legacy analysis codes are frequently unsuitable for use in automated design. They may crash for large classes of input, be numerically unstable or locally non-smooth, or be highly sensitive to control parameters. To be useful, analysis programs must be modified to reduce or eliminate only the undesired behaviors, without altering the desired computation. To do this by direct modification of the programs is labor-intensive, and necessitates costly revalidation. We have implemented a high-level language and run-time environment that allow failure-handling strategies to be incorporated into existing Fortran and C analysis programs while preserving their computational integrity. Our approach relies on globally managing the execution of these programs at the level of discretely callable functions so that the computation is only affected when problems are detected. Problem handling procedures are constructed from a knowledge base of generic problem management strategies. We show that our approach is effective in improving analysis program robustness and design optimization performance in the domain of conceptual design of jet engine nozzles. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #315789. 	[Title] A Transformation System for Interactive Reformulation of Design Optimization Strategies [Abstract] Automatic design optimization is highly sensitive to problem formulation. The choice of objective function, constraints and design parameters can dramatically impact the computational cost of optimization and the quality of the resulting design. The best formulation varies from one application to another. A design engineer will usually not know the best formulation in advance. In order to address this problem, we have developed a system that supports interactive formulation, testing and reformulation of design optimization strategies. Our system includes an executable, data-flow language for representing optimization strategies. The language allows an engineer to define multiple stages of optimization, each using different approximations of the objective and constraints or different abstractions of the design space. We have also developed a set of transformations that reformulate strategies represented in our language. The transformations can approximate objective and constraint functions, abstract or re-parameterize a search space, or divide an optimization into multiple stages. The system is applicable to design problems in which the artifact is governed by algebraic and ordinary differential equations. We have tested the system on problems of racing yacht and jet engine nozzle design. We report experimental results demonstrating that our reformulation techniques can significantly improve the performance of automatic design optimization. Our research demonstrates the viability of a reformulation methodology that combines symbolic program transformation with numerical experimentation. It is an important first step in a research programme aimed at automating the entire strategy formulation process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1138027. 	[Title] Problem Formulation, Program Synthesis and Program Transformation Techniques for Simulation, Optimization and Constraint Satisfaction (Research Statement) [Abstract]  Please summarise all the given information.

Paper #1139195 has the title `Program Synthesis and Transformation Techniques for Simpuation, Optimization and Constraint Satisfaction Deductive Synthesis of Numerical
` and the abstract `Scientists and engineers face recurring problems of constructing, testing and modifying numerical simulation programs. The process of coding and revising such simulators is extremely time-consuming, because they are almost always written in conventional programming languages. Scientists and engineers can therefore benefit from software that facilitates construction of programs for simulating physical systems. Our research adapts the methodology of deductive program synthesis to the problem of constructing numerical simulation codes. We have focused on simulators that can be represented as second order functional programs composed of numerical integration and root extraction routines. We have developed a system that uses first order Horn logic to synthesize numerical simulators built from these components. Our approach is based on two ideas: First, we axiomatize only the relationship between integration and differentiation. We neither attempt nor require a complete axiomatization of mathematical analysis. Second, our system uses a representation in which functions are reified as objects. Function objects are encoded as lambda expressions. Our knowledge base includes an axiomatization of term equality in the lambda calculus. It also includes axioms defining the semantics of numerical integration and root extraction routines. We use depth bounded SLD resolution to construct proofs and synthesize programs. Our system has successfully constructed numerical simulators for computational design of jet engine nozzles and sailing yachts, among others. Our results demonstrate that deductive synthesis techniques can be used to construct numerical simulation programs for realistic applications (Ellman and Murata 1998). Automatic design optimization is highly sensitive to problem formulation. The choice of objective function, constraints and design parameters can dramatically impact the computational cost of optimization and the quality of the resulting design. The best formulation varies from one application to another. A design engineer will usually not know the best formulation in advance. In order to address this problem, we have developed a system that supports interactive formulation, testing and reformulation of design optimization strategies. Our system includes an executable, data-flow language for representing optimization strategies. The language allows an engineer to define multiple stages of optimization, each using different approximations of the objective and constraints or different abstractions of the design space. We have also developed a set of transformations that reformulate strategies represented in our language. The transformations can approximate objective and constraint functions, abstract or reparameterize search spaces, or divide an optimization process into multiple stages. The system is applicable in principle to any design problem that can be expressed in terms of constrained op 
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Question: Which category should Paper #1139195 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1139195 has the title `Program Synthesis and Transformation Techniques for Simpuation, Optimization and Constraint Satisfaction Deductive Synthesis of Numerical
` and the abstract `Scientists and engineers face recurring problems of constructing, testing and modifying numerical simulation programs. The process of coding and revising such simulators is extremely time-consuming, because they are almost always written in conventional programming languages. Scientists and engineers can therefore benefit from software that facilitates construction of programs for simulating physical systems. Our research adapts the methodology of deductive program synthesis to the problem of constructing numerical simulation codes. We have focused on simulators that can be represented as second order functional programs composed of numerical integration and root extraction routines. We have developed a system that uses first order Horn logic to synthesize numerical simulators built from these components. Our approach is based on two ideas: First, we axiomatize only the relationship between integration and differentiation. We neither attempt nor require a complete axiomatization of mathematical analysis. Second, our system uses a representation in which functions are reified as objects. Function objects are encoded as lambda expressions. Our knowledge base includes an axiomatization of term equality in the lambda calculus. It also includes axioms defining the semantics of numerical integration and root extraction routines. We use depth bounded SLD resolution to construct proofs and synthesize programs. Our system has successfully constructed numerical simulators for computational design of jet engine nozzles and sailing yachts, among others. Our results demonstrate that deductive synthesis techniques can be used to construct numerical simulation programs for realistic applications (Ellman and Murata 1998). Automatic design optimization is highly sensitive to problem formulation. The choice of objective function, constraints and design parameters can dramatically impact the computational cost of optimization and the quality of the resulting design. The best formulation varies from one application to another. A design engineer will usually not know the best formulation in advance. In order to address this problem, we have developed a system that supports interactive formulation, testing and reformulation of design optimization strategies. Our system includes an executable, data-flow language for representing optimization strategies. The language allows an engineer to define multiple stages of optimization, each using different approximations of the objective and constraints or different abstractions of the design space. We have also developed a set of transformations that reformulate strategies represented in our language. The transformations can approximate objective and constraint functions, abstract or reparameterize search spaces, or divide an optimization process into multiple stages. The system is applicable in principle to any design problem that can be expressed in terms of constrained op 
`.
Question: Which category should Paper #1139195 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Case_Based