Abstract: This invention presents an innovative framework for the application of machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment, process, microstructure, temperature, strain rate, environment or testing mode. Our framework assumes selection of optimization technique suitable for the application at hand and data available, starting with simple linear, or quadratic, regression analysis. We present a physics-based model for predicting the ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying physics as a priori information.

Abstract: 1. The invention presents a mechanism for assessing student designs against learning outcomes from the Accreditation Board for Engineering and Technology. Changes in formulation of the learning outcomes can be simply accommodated by changing association between the learning outcomes and performance indicators, but keeping assessment rubrics the same. 2. We present a plugin for automatically verifying engineering requirements formulated in the SysML system modeling language. The plugin employs a generic verification method consisting of check points systematically positioned along the function chain. 3. Smart Modeling and Simulation is an automated system for creating simulation models, performing rapid simulations and interactively interpreting the results. It can account for complex multi-physics interactions not traditionally supported by commercial analysis software. 4.

Abstract: This invention presents a framework for applying artificial intelligence to aid with product design, mission or retail planning. The invention outlines a novel approach for applying predictive analytics to the training of a system model for product design, assimilates the definition of meta-data for design containers to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with relational dependence. Design information can be harvested, for the purpose of improving decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis, predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user.

Abstract: This invention presents an innovative framework for the application of machine learning (ML) for identification of energetic materials with desired properties of interest. For the output properties of interest, we identify the corresponding driving (input) factors. We present a framework for a generic engine for predicting properties of energetic materials, once capable of interacting with and receiving support from physics-based prediction models, supporting joint optimization, accounting for properties both at macro- and micro-level, supporting multi-linear regression of descriptors, and offering physics-based interpretation of the descriptors. We present an approach for formulating descriptors, capable of both capturing properties and behavior of complex molecular structures, and that can be imported into ML algorithms for analysis.

Abstract: This invention presents a transformative design decision support tool (an e-Design Assessment Engine), one that 1. Significantly reduces cost and risk of engineering design projects, over the state of affair, in part through early and automatic detection of design oversights; 2. Offers compelling reduction in the development time of designs, through improved productivity, but without compromising quality or creativity; 3. Offers wide range of flexibility in terms of supporting design processes employed at different design organizations. The design oversights are identified through proper structuring of the engineering design requirements, extraction of relevant design parameters through application program interfaces provided by the pertinent design tools, and mapping against the requirements. Big data analytics are applied to repositories of past designs, for the purpose of improving new designs.

Abstract: This invention presents an innovative framework for the application of machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment, process, microstructure, temperature, strain rate, environment or testing mode. Our framework assumes selection of optimization technique suitable for the application at hand and data available, starting with simple linear, or quadratic, regression analysis. We present a physics-based model for predicting the ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying physics as a priori information.

Abstract: This invention presents a framework for applying artificial intelligence to aid with product design, mission or retail planning. The invention outlines a novel approach for applying predictive analytics to the training of a system model for product design, assimilates the definition of meta-data for design containers to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with relational dependence. Design information can be harvested, for the purpose of improving decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis, predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user.

Abstract: This invention presents Ecosystems addressing limited capabilities for interactive team design, amidst thrust for improved productivity, enhanced creativity and higher quality. The Ecosystem for Team Design and Collaboration presents apparatus for taking snapshots of whiteboards with sketches from planning, design or brainstorming meetings, using image sensor embedded in a mobile client, for offering a vector representation of the content and hence expediting the preparation of ‘presentable entities’ (eliminating the need to redraw). The Ecosystem for Learning and Team Design outlines a flexible design methodology for automatically assessing the quality of design activities relative to a given design process. It is the only learning tool for computer aided design (CAD) capable of assessing all the five design stages. Its assessment engine, the e-design process, is modular and can be integrated into electronic lab notebooks.

Abstract: This invention presents Ecosystems addressing limited capabilities for interactive team design, amidst thrust for improved productivity, enhanced creativity and higher quality. The Ecosystem for Team Design and Collaboration presents apparatus for taking snapshots of whiteboards with sketches from planning, design or brainstorming meetings, using image sensor embedded in a mobile client, for offering a vector representation of the content and hence expediting the preparation of ‘presentable entities’ (eliminating the need to redraw). The Ecosystem for Learning and Team Design outlines a flexible design methodology for automatically assessing the quality of design activities relative to a given design process. It is the only learning tool for computer aided design (CAD) capable of assessing all the five design stages. Its assessment engine, the e-design process, is modular and can be integrated into electronic lab notebooks.

Abstract: This invention implements a system for automatic recognition of human-assisted drawings, in a plurality of forms, be they hand-drawn on paper, marker board, with a stylus on a computer, made with a mouse, stylus, finger or other instrument on a personal computer, tablet computer, smart telephone or other medium. At the core of the invention is a pattern recognition engine, aimed at recognizing the graphical objects, handwritten text, equations or interconnects in the input image, and interpreting the significance of their relative association. The apparatus offers error correction, vector representation of the input sketch, as intermediate output, along with the recognized patterns, arranged in a hierarchical data structure, ready to be passed on for mining or assessment.