219,199 research outputs found
Conceptual Design Generation Using Large Language Models
Concept generation is a creative step in the conceptual design phase, where
designers often turn to brainstorming, mindmapping, or crowdsourcing design
ideas to complement their own knowledge of the domain. Recent advances in
natural language processing (NLP) and machine learning (ML) have led to the
rise of Large Language Models (LLMs) capable of generating seemingly creative
outputs from textual prompts. The success of these models has led to their
integration and application across a variety of domains, including art,
entertainment, and other creative work. In this paper, we leverage LLMs to
generate solutions for a set of 12 design problems and compare them to a
baseline of crowdsourced solutions. We evaluate the differences between
generated and crowdsourced design solutions through multiple perspectives,
including human expert evaluations and computational metrics. Expert
evaluations indicate that the LLM-generated solutions have higher average
feasibility and usefulness while the crowdsourced solutions have more novelty.
We experiment with prompt engineering and find that leveraging few-shot
learning can lead to the generation of solutions that are more similar to the
crowdsourced solutions. These findings provide insight into the quality of
design solutions generated with LLMs and begins to evaluate prompt engineering
techniques that could be leveraged by practitioners to generate higher-quality
design solutions synergistically with LLMs.Comment: Proceedings of the ASME 2023 International Design Engineering
Technical Conferences and Computers and Information in Engineering
Conference
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
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Harnessing agile concepts for the development of intelligent systems
Traditional and current approaches to intelligent systems design, have led to the creation of sophisticated and computationally-intensive packages and environments, for a wide range of applications. This paper proposes methods with which to extend the functionality of such systems, borrowing knowledge management concepts from the field of Agile Manufacturing. As such, this paper proposes that the future of intelligent systems design should be based not only upon the continuing development of artificial intelligence techniques, but also effective methods for harnessing human skills and core competencies to achieve these aims
A knowledge-based geometry repair system for robust parametric CAD models
In modern multi-objective design optimization (MDO) an effective geometry engine is
becoming an essential tool and its performance has a significant impact on the entire MDO
process. Building a parametric geometry requires difficult compromises between the conflicting
goals of robustness and flexibility. This article presents a method of improving the
robustness of parametric geometry models by capturing and modeling engineering knowledge
with a support vector regression surrogate, and deploying it automatically for the
search of a more robust design alternative while trying to maintain the original design
intent. Design engineers are given the opportunity to choose from a range of optimized
designs that balance the âhealthâ of the repaired geometry and the original design intent.
The prototype system is tested on a 2D intake design repair example and shows the potential
to reduce the reliance on human design experts in the conceptual design phase and
improve the stability of the optimization cycle. It also helps speed up the design process
by reducing the time and computational power that could be wasted on flawed geometries
or frequent human intervention
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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