Abstract: A computer-implemented search and poll method can iteratively solve a multi-fidelity optimization problem including an objective function and any constraints. A search step of the method includes constructing and optimizing surrogates of the objective function and any constraints to identify a new set of trial points, and running lower fidelity simulations in ascending order to reduce a number of trial points in the new set. The search step further includes evaluating the reduced number of trial points with the objective function and any constraints using a high fidelity simulation.

Abstract: A design process that uses lamination parameter inversion to generate a set of baseline layups having desired stiffness properties. Then the underdetermined Newton's method can be applied to explore solution manifolds describing alternative designs having similar if not identical stiffness properties. The manifold of solutions can be methodically examined to find those with desirable properties. Desirable properties include those that have been traditionally captured by design rules or those that improve manufacturability. Combining lamination parameters as design variables with lamination parameter inversion provides an efficient optimization process for non-traditional laminates.

Abstract: A design process that uses lamination parameter inversion to generate a set of baseline layups having desired stiffness properties. Then the underdetermined Newton's method can be applied to explore solution manifolds describing alternative designs having similar if not identical stiffness properties. The manifold of solutions can be methodically examined to find those with desirable properties. Desirable properties include those that have been traditionally captured by design rules or those that improve manufacturability. Combining lamination parameters as design variables with lamination parameter inversion provides an efficient optimization process for non-traditional laminates.

Abstract: A computer-implemented search and poll method can iteratively solve a multi-fidelity optimization problem including an objective function and any constraints. A search step of the method includes constructing and optimizing surrogates of the objective function and any constraints to identify a new set of trial points, and running lower fidelity simulations in ascending order to reduce a number of trial points in the new set. The search step further includes evaluating the reduced number of trial points with the objective function and any constraints using a high fidelity simulation.

Abstract: A system for visualizing and optimizing the operation of a system of systems. A Design of Experiments is conducted for a particular system of systems architecture using a simulation engine based upon a predefined set of inputs. The results are stored in a database as real data. Surface Response Models are built based upon the results and simulation data is generated and stored based on the Surface Response Models for data not present in the results. The results of the Design of Experiments are filtered to identify key variables. Values and rankings are selected for the key variables. An interim output is generated and displayed based on the selected values and rankings. The interim output is determined based on the real and simulation data. The results are verified by performing a simulation based on the selected values and comparing the simulation output with the interim output.