2000 - Fellow of the American Society of Mechanical Engineers
His primary areas of study are Engineering design process, Simulated annealing, Artificial intelligence, Computer Aided Design and Mathematical optimization. His work on Probabilistic design as part of general Engineering design process study is frequently linked to Fixation, bridging the gap between disciplines. Jonathan Cagan has included themes like Mechanical engineering and Very-large-scale integration in his Simulated annealing study.
His work deals with themes such as Cognitive psychology, Creativity, Function, Product design and Novelty, which intersect with Artificial intelligence. In Computer Aided Design, Jonathan Cagan works on issues like Engineering drawing, which are connected to Grammar, Shape grammar, Brand identity and Representation. His Mathematical optimization study combines topics from a wide range of disciplines, such as Turbine, Turbine blade and Component.
Jonathan Cagan focuses on Engineering design process, Artificial intelligence, Mathematical optimization, Shape grammar and Simulated annealing. The concepts of his Engineering design process study are interwoven with issues in Management science, Conceptual design, Human–computer interaction, Systems engineering and Process. His Artificial intelligence study combines topics in areas such as Machine learning, Creativity, Function and Natural language processing.
His Pattern search and Stochastic optimization study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Numerical analysis, bridging the gap between disciplines. His Shape grammar research incorporates elements of Interpreter and Engineering drawing. His Simulated annealing research includes themes of Computer Aided Design and Annealing.
Jonathan Cagan mainly investigates Artificial intelligence, Human–computer interaction, Engineering design process, Mathematical optimization and Heuristic. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Grammar. His Human–computer interaction research incorporates themes from Test, Differential game, Task and Mechanism.
His study in Engineering design process is interdisciplinary in nature, drawing from both Design strategy, Engineering management, Configuration design, Transfer of learning and Conceptual design. His Mathematical optimization research focuses on Stackelberg competition and how it relates to Adversarial system. His work carried out in the field of Learning curve brings together such families of science as Shape grammar and Rule-based machine translation.
The scientist’s investigation covers issues in Engineering design process, Human–computer interaction, Artificial intelligence, Neuroimaging and Conceptual design. His biological study spans a wide range of topics, including Domain, Design strategy, Engineering management, Transfer of learning and Process management. The Human–computer interaction study combines topics in areas such as Crowdsourcing, Test, Interface and Mechanism.
When carried out as part of a general Artificial intelligence research project, his work on Learning classifier system is frequently linked to work in Computational design, therefore connecting diverse disciplines of study. His Neuroimaging research spans across into subjects like Cognitive science, Cognitive psychology, Meaning, Problem space and Brain activation. His research in Conceptual design intersects with topics in Configuration design, Management science, Process and Diversity.
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Creating Breakthrough Products: Innovation from Product Planning to Program Approval
Jonathan Cagan;Craig M. Vogel.
A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty
J. S. Linsey;I. Tseng;K. Fu;J. Cagan.
Journal of Mechanical Design (2010)
Speaking the Buick Language: Capturing, Understanding, and Exploring Brand Identity With Shape Grammars
Jay P McCormack;Jonathan Cagan;Craig M Vogel.
Design Studies (2004)
Formal Engineering Design Synthesis
Erik K. Antonsson;Jonathan Cagan.
On the benefits and pitfalls of analogies for innovative design : Ideation performance based on analogical distance, commonness, and modality of examples
Joel Chan;Katherine Fu;Christian D. Schunn;Jonathan Cagan.
Journal of Mechanical Design (2011)
A Blend of Different Tastes: The Language of Coffeemakers:
Manish Agarwal;Jonathan Cagan.
Environment and Planning B-planning & Design (1998)
A survey of computational approaches to three-dimensional layout problems
Jonathan Cagan;Kenji Shimada;Sun Yin.
Computer-aided Design (2002)
Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar
Michael J. Pugliese;Jonathan Cagan.
Research in Engineering Design (2002)
A-Design: An Agent-Based Approach to Conceptual Design in a Dynamic Environment
Matthew I. Campbell;Jonathan Cagan;Kenneth Kotovsky.
Research in Engineering Design (1999)
The role of timing and analogical similarity in the stimulation of idea generation in design
Ian Tseng;Jarrod Moss;Jonathan Cagan;Kenneth Kotovsky.
Design Studies (2008)
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