Functional thinking in cost estimation through the tools and concepts of axiomatic design

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (leaf 27).There has been an increasing demand for cost estimation tools which aid in the reduction of system cost or the active consideration of cost as a design constraint. The existing tools are currently incapable of anticipating the unseen or latent effects of design changes made in an effort to cut cost. This paper presents an example of how the tools and concepts of axiomatic design theory can be integrated with the parametric cost estimation process, and then presents a series of arguments for why tools such as these which examine the functional architecture of a system are useful for optimizing cost at the preliminary design level.by Lael Ulam Odhner.S.B

Conceptual understanding through efficient automated design of quantum optical experiments

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level

Modeling and Reasoning with Multisets and Multirelations in Alloy

Multisets and multirelations arise naturally in modeling; however, most modeling languages either have limited or completely lack support for multisets and multirelations. Alloy, for instance, is a lightweight relational modeling language which provides automatic analysis of models. In Alloy, ordinary sets and relations are the only first-class language semantic constructs; therefore to work with multisets and multirelations, modelers need to invent ad-hoc ways to encode these multiconcepts or rely on a third-party library that provides their implementations, assuming there is such one. In fact, such a library has been missing for Alloy, and implementing a fully functional multiconcepts library is challenging, especially when it is required to encode an algebra of operations over multiconcepts. This thesis presents two sound and practical mathematical formalizations of multiconcepts, namely, index-based and multiplicity-based, which encode multisets and multirelations using only basic concepts such as ordinary sets, total functions and natural numbers. We implement two generic multiconcepts libraries in Alloy based on the corresponding formalizations. Each library has a carefully designed interface and can be seamlessly integrated into existing relational models. We also perform an empirical evaluation on both implementations; the result shows multiplicity-based encoding is more scalable in terms of performance; thus, it is more preferable in practice