| description abstract | The Journal of Computing and Information Science in Engineering (JCISE) was launched in the year 2001 as a transactions journal of the American Society of Mechanical Engineers (ASME), thanks to the team effort led by the ASME Computers and Information in Engineering Division and spearheaded by the Division's executive committee members, including Simon Szykman, Yong-Se Kim, and David Rosen. Jami Shah served as the first Editor-in-Chief from 2001 to 2011. Four issues were published each year during the first decade of shaping up. The journal later became jointly sponsored by the ASME Design Engineering Division in 2005.The first issue in the first volume of the journal was a collection of 12 position and review articles on the topics of artificial intelligence (AI) and knowledge-based systems, product lifecycle management, engineering information systems and standards, computer-aided design, geometric modeling, feature recognition, computer-aided manufacturing, rapid prototyping, virtual reality applications in engineering, and modeling and simulation. In its earliest years, the journal was under the shadow of the dot.com boom and bust, which was also met with resistance and trepidation from some engineering researchers who regarded computing-related articles as simple number crunching, lacking mathematical or theoretical foundations, thereof archival value, and prone to quick obsolescence. Nevertheless, history has proven that most of the topics included in the first issue of the journal remain active research areas that are still being pursued by the research community 25 years later.Despite the fact that today's artificial intelligence breakthroughs, such as large language models (LLMs), were not foreseen by many of us even a few years ago, a statement in the editorial of the first volume-first issue is just as relevant: “The new economy is driven by technological innovations in computing, electronic communications, and information science. Mechanical engineers are using new software and IT tools in design, analysis, procurement, and manufacturing to reduce product development cost and time by orders of magnitude, and to explore large numbers of alternatives via virtual and rapid prototyping. Mechanical engineers are not only using these technologies, but they are also involved in their development in collaboration with computer scientists and IT researchers.”In its first decade, JCISE quickly became a premier archival venue for research in computational methods and information technology with applications to mechanical design, analysis, simulation, and manufacturing. The emphasis on product design and manufacturing, combined with a focus on fundamental computational principles, gave the journal a unique distinction among computational publications, establishing it as the central forum for disseminating high-quality research results within the mechanical engineering community.The second Editor-in-Chief of JCISE is Bahram Ravani, who served from 2011 to 2017. During this period, the journal's scope was gradually expanded to reflect the evolving directions in computational and information science in engineering. New areas such as embedded systems, computational kinematics, cloud computing, advanced data management, such as semantic databases, and the use of specialized hardware architectures, such as graphics processing units (GPUs) and supercomputing platforms, were incorporated. The journal's focus was also broadened to encompass the engineering of complex systems, systems engineering, and enterprise engineering, reflecting the growing interdisciplinary nature of this field.Since its inception, JCISE has also been a pioneer in digital publishing. It was the first ASME journal to adopt fully electronic submission and review processes through eLane, a system developed by Jami Shah and collaborators in 2001. This innovation not only streamlined the review workflow but also helped accelerate ASME's transition to its current Journal Tool platform for digital publishing. The first editorial initiative of Bahram Ravani was to lead JCISE's migration to the Journal Tool, formally integrating it into ASME's standard technical publishing operations.During this period, JCISE experienced significant growth in both visibility and impact. Article submissions increased, and article publications became more selective. The journal's impact factor doubled. This growth reflected the recognition of JCISE by the technical community as the preferred venue for archival research at the intersection of computing and mechanical engineering.In 2017, SK Gupta assumed the leadership role of Editor-in-Chief. During his tenure from 2017 to 2022, the field of computing and information science continued to experience significant growth, especially because of the growing popularity of machine learning and artificial intelligence. The scope of the journal was expanded to better serve the community. The renewed journal scope covered advances in algorithms, computational methods, computing infrastructure, computer-interpretable representations, human–computer interfaces, information science, and/or system architectures that aim to improve some aspects of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling). The published articles focus on either fundamental research leading to new methods or the adaptation of existing methods for new applications. Many new keywords were added to cover new focus areas. The process of updating the journal scope and keywords was done by consulting a large number of researchers from the ASME community. The editorial board was expanded considerably to handle the expanded scope of the journal.During this period, the journal received an increased number of articles related to machine learning and artificial intelligence with applications to a wide variety of topics, such as design, engineering analysis, manufacturing, and metrology. Physics-informed machine learning was directly or indirectly emphasized in many of the published articles. We also witnessed increased attention being paid to cyber-physical systems and novel computing architectures, such as cloud computing and GPU-accelerated computing. A large number of submissions focused on human–machine collaboration or human–machine interactions, many of which leveraged advances in augmented and virtual reality. Digital twins and digital thread topics also emerged. As cybersecurity started being acknowledged as a major threat, this topic received some attention in the journal.The growth in submissions led the journal to be published six times per year. This allowed us to feature more special issues every year. This period is also parallel with an increase in the popularity of social media. The journal expanded its presence on social media by posting on LinkedIn and offering webinars that featured spotlight talks from the journal. A companion website1 was also established.In 2022, Yan Wang started serving as the fourth Editor-in-Chief of JCISE. The number of submissions continued to grow and reached a record high of 700 in 2024, and the journal started publishing 12 issues in 2024 to meet the publication needs of the community. The growth was mostly powered by the emerging topics of AI and machine learning, such as generative AI models, knowledge graphs, physics-informed machine learning, neural operator learning, and physical AI, as well as the revival of earlier AI methods, such as multi-agent systems and hybrid learning and rule-based systems. Articles with the emerging topic of quantum computing were also published.A number of special issues were published between 2022 and 2025, which demonstrated the expanded interests of the research community. These special issues focused on several forward-looking research topics. The success of publications reflected the cutting-edge research efforts in the field, such as machine intelligence for engineering under uncertainties, data representation for machine learning, extended reality in design and manufacturing, symbiotic human–artificial intelligence partnership, human–robot collaboration in industry 5.0, geometric data processing for advanced manufacturing, digitalization in reverse engineering, digitalization in energy systems, large language models in design and manufacturing, cybersecurity in manufacturing, scientific machine learning, physics-informed machine learning, networks and graphs in systems engineering, and generative artificial intelligence for design, manufacturing, and materials. Many of the special issues were the first collections of their kind published by ASME or other engineering journals.JCISE has become a leading journal in the field of engineering computation that goes beyond mechanical engineering. It has attracted high-quality article submissions from other relevant domains, including aerospace engineering, civil engineering, industrial engineering, systems engineering, materials science, computational science, and applied mathematics. To provide a comprehensive and clear overview of the scope of JCISE to the readers and authors, 12 thrust areas of applications were formed and clustered. They are computer-aided design and manufacturing, computational geometry and geometry processing, cyber-physical-social systems, data analytics and machine learning, engineering optimization, human–computer interface and human modeling, intelligent manufacturing, machine intelligence and robotics systems, modeling and simulation and scientific computing, precision engineering and reverse engineering, sustainability and product lifecycle management, and systems engineering and engineering informatics, respectively. JCISE emphasizes the contributions of new methodologies in either mathematical modeling or computational approaches as the requirement of research articles to be published in the journal, with any of the above 12 application areas.JCISE has been fortunate with generous support from over 90 dedicated associate editors in the past 25 years. They are Gaurav Ameta (Siemens Corporate Technology), Nabil Anwer (LURPA—Ecole Normale Superieure Paris-Saclay), Stephen Baek (University of Virginia), William Bernstein (Air Force Research Laboratory), Linkan Bian (Mississippi State University), Monica Bordegoni (Politecnico di Milano), Sean Callahan (The Boeing Company), Matthew I. Campbell (Oregon State University), Yong Chen (University of Southern California), Harry Cheng (University of California, Davis), Seung-Kyum Choi (Georgia Institute of Technology), Chih-Hsing Chu (National Tsing Hua University), Richard Crawford (University of Texas at Austin), Jonathan R. Corney (University of Edinburgh), Kaushal Desai (India Institute of Technology, Jodhpur), Nancy Dorighi (NASA Ames), Deba Dutta (University of Michigan), Ehsan Esfahani (State University of New York at Buffalo), Francesco Ferrise (Politecnico di Milano), Anath Fischer (Technicon), Amir Gandomi (University of Technology Sydney), Shuming Gao (Zhejiang University), Chris Geiger (University of Applied Sciences, Dusseldorf), Ashok K. Goel (Georgia Institute of Technology), Ian Grosse (University of Massachusetts-Amherst), Johann Guilleminot (Duke University), Satyandra Kumar Gupta (University of Southern California), Balan Gurumoorthy (Indian Institute of Science), Bin He (Shanghai University), Sankar Jayaram (Washington State University), Ajay Joneja (Hong Kong University of Science and Technology), Leo Joskowicz (Hebrew University of Jerusalem), Krishnanand Kaipa (Old Dominion University), Pradeep Khosla (Carnegie Mellon University), Yoshinobu Kitamura (Osaka University), Vinayak Krishnamurthy (Texas A&M University), Ashok V. Kumar (University of Florida), Tsz-Ho Kwok (Concordia University), Kincho Law (Stanford University), Kunwoo Lee (Seoul National University), Dan Li (University of Wisconsin-Madison), Guang Lin (Purdue University), Ying Liu (Cardiff University), Yusheng Liu (Zhejiang University), Yan Lu (National Institute of Standards and Technology), Jianxi Luo (City University of Hong Kong), Yongsheng Ma (Southern University of Science and Technology), Mahesh Mani (National Institute of Standards and Technology), Martti Mantyla (Helsinki University of Technology), Chris McMahon (University of Bath), Sara McMains (University of California, Berkeley), John Michopoulos (U.S. Naval Research Laboratory), Samy Missoum (University of Arizona), Duhwan Mun (Korea University), Dana Nau (University of Maryland), Saigopal Nelaturi (Palo Alto Research Center), Alison Olechowski (University of Toronto), James H. Oliver (Iowa State University), Yayue Pan (University of Illinois at Chicago), Jitesh H. Panchal (Purdue University), Nicholas Patrikalakis (Massachusetts Institute of Technology), Chris Paredis (Clemson University), Anurag Purwar (Stony Brook University), Xiaoping Qian (University of Wisconsin-Madison), Rahul Rai (Clemson University), Ravi Rangan (Centric Software Inc.), P.V.M. Rao (India Institute of Technology, Delhi), Bahram Ravani (University of California, Davis), David Rosen (Georgia Institute of Technology), Caterina Rizzi (University of Bergamo), Kazuhiro Saitou (University of Michigan), Vadim Shapiro (University of Wisconsin-Madison), Shana Smith (National Taiwan University), Yu Song (Delft University of Technology), Vijay Srinivasan (National Institute of Standards and Technology), Joshua Summers (University of Texas-Dallas), Hongyue Sun (University of Georgia), Krishnan Suresh (University of Wisconsin-Madison), Simon Szykman (National Institute of Standards and Technology), Atul Thakur (Indian Institute of Technology, Patna), Wenmeng Tian (Mississippi State University), Conrad S. Tucker (Carnegie Mellon University), Cameron Turner (Clemson University), Susan Urban (Arizona State University), Douglas Van Bossuyt (Naval Postgraduate School), Jan Vandenbrande (The Boeing Company), Charlie C.L. Wang (University of Manchester), Jian-Xun Wang (Cornell University), Jun Wang (Nanjing University of Aeronautics and Astronautics), Yan Wang (Georgia Institute of Technology), Kristina Wärmefjord (Chalmers Institute of Technology), Paul Witherell (National Institute of Standards and Technology), Paul Wright (University of California, Berkeley), Yisha Xiang (University of Houston), Hui Yang (Pennsylvania State University), Xiaowei Yue (Tsinghua University), Zhinan Zhang (Shanghai Jiao Tong University), and Yaoyao Fiona Zhao (McGill University). We would like to express our gratitude to all associate editors as well as all guest editors, authors, and reviewers for shaping and defining the JCISE community.To celebrate the 25th anniversary of JCISE, we offer this special issue with a collection of 13 review and position articles. The goal is to provide some prospects of research opportunities for engineering computation in the near future. There is no doubt that computing and information science will continue to be the driving force of productivity in modern society. The thriving advancement and fruitful outcome of computation-related efforts will persist in engineering research.AI and machine learning are regarded by many as the most important subjects for research in engineering computation in the coming years. The enthusiasm is evident from all the articles included in this special issue. In the article entitled “Expanding the Generative Power of Large Language Models for Design Through Formal Design Grammars and Languages,” Shea et al. outlined the potential of LLMs to act as collaborators of human designers to develop design grammars as rules for sequential and structured generation of designs. LLMs can serve not only as a guide and partner to develop grammar with their generative capability, but also as a grammar interpreter to convert natural language descriptions of design grammars to executable computer code. Future research on comparison, evaluation, and validation of LLM-based design grammars is highlighted by the authors. In the article entitled “Intelligent Design 4.0: Paradigm Evolution Toward the Agentic Artificial Intelligence Era,” Jiang et al. postulated an Intelligent Design 4.0 paradigm that integrates agentic AI into design processes as a transition from data-driven assistance to multi-agent autonomy. The multi-agent AI system includes various collaborative agents, such as requirement analysis, concept generation, embodiment generation, detailed modeling, and design optimization corresponding to different design stages. Research challenges related to embedding physical knowledge and laws, information exchange between symbolic solvers and token-based models, and alignment with human values are foreseen.For engineering applications of artificial intelligence and machine learning, the considerations of physical knowledge and physical space are critical. In the article entitled “Physical Artificial Intelligence for Powering Next Revolution in Robotics,” Thakur et al. articulated a unifying perspective on physical artificial intelligence with strong capabilities of sensorimotor coupling, learning-in-the-loop, and human interaction. The applications of physical AI will be ubiquitous in manufacturing, healthcare, agriculture, logistics, and transportation. Several challenges and future opportunities are outlined, including real-time data processing, energy efficiency, cybersecurity, safety, and ethical considerations. In the article entitled “Physics-Informed Machine Learning in Design and Manufacturing: Status and Challenges,” Pan et al. provided a comprehensive review of research on physics-informed machine learning, also known as scientific machine learning, occurred in the past three decades. The approaches are generally categorized into hybrid models (e.g., residual modeling, feature engineering), physical loss-based models (e.g., multifidelity and multiphysics physics-informed neural networks), and physics-embedded architectures (e.g., hard-constrained, graph-based, generative, and operator learning, dictionary learning). Major challenges for industry-scale applications include training convergence, data imperfection, physics-data conflicts, generalizability, and extrapolation capability are highlighted. Future opportunities of uncertainty quantification, reduced-order modeling, and optimization algorithms are worthy of further exploration. In the article entitled “A Review of Artificial Intelligence-Driven Approaches for Nanoscale Heat Conduction and Radiation,” Guo et al. provided a thorough review of the state-of-the-art applications of artificial intelligence and machine learning techniques for nanoscale heat transfer modeling, ranging from machine learning interatomic potentials in molecular dynamics simulation to data-driven solutions to Maxwell's equations, and generative design of radiative energy devices. It has been pointed out that the lack of annotated high-quality datasets, the generalization capabilities of models, and their lack of transparency and interpretability are the major challenges for engineering applications such as heat transfer modeling.In the article entitled “Toward Smart Manufacturing Metaverse Via Digital Twinning in Extended Reality,” Yang et al. provided an overview of the emerging human-centered manufacturing metaverse based on extended reality (virtual reality, augmented reality, and mixed reality) as well as artificial intelligence and digital twin methodologies. 3D immersive environments enable unprecedented opportunities of remote collaboration and prototyping, virtual learning and walkthrough. Research challenges and opportunities coexist, such as interpretability between generative AI and existing enterprise information systems and digital twins, metaverse cybersecurity and privacy, integration of heterogeneous agents, and manufacturing-as-a-service sharing economy. In the article authored by Aruanno et al., entitled “Extended Reality in Industry and Healthcare: Current Trends and Future Perspectives,” extended reality is framed as a technological enabler for product design, training, manufacturing, and healthcare, and will be an integrated platform of artificial intelligence, digital twins, and multisensory emersion. Yet, several research challenges remain, such as spatial intelligence, dynamic content generation, interoperability and scaling across organizations, intelligent adaptation to individual user behaviors, ethics, and mental health impact. In the article entitled “Advances in Modeling, Analysis, and Control of Manufacturing Systems: Methods, Gaps, and Research Frontiers,” Chang et al. gave a comprehensive review of systems-level modeling, analysis, and control methodologies for manufacturing systems. A variety of current approaches were summarized, ranging from traditional models, such as Markov chains, discrete-event simulation, network theoretic, model predictive control, to the latest digital twin, artificial intelligence, and machine learning approaches. The future research opportunities of system and process control are identified, including interpretability and safety associated with the foundational models such as large language models and hybrid reinforcement learning, as well as uncertainty, scalability, and integration in industrial applications.Data have been a critical component of artificial intelligence and machine learning. In the article entitled “Principles and Metrics for Curating Large Engineering Simulation Data Sets for ML,” Shah et al. provided a comprehensive overview of existing public datasets for engineering design, outlined the principles of curating engineering simulation data with the key attributes of data formats, modalities, and granularities, and further proposed several efficacy metrics of large datasets for machine learning to characterize data size, balance, and variety. It is also expected that generative AI for synthetic data generation with spatial reasoning capabilities, self-curating data pipelines, federated and privacy-preserving learning, and new data quality standards will emerge in future research directions. In the article entitled “Advancements in Kinematic Synthesis: Data-Driven Methods for Mechanism Design,” Purwar and Ge presented a historical and forward-looking overview of data-enriched deep learning techniques for the kinematic synthesis of mechanisms to generate designs with diverse types and geometries. A research roadmap is provided to address the research challenges of data size, balance, bias, generality, scalability, and validation. There are imminent needs of structure-aware encodings, multi-task learning, large language models as copilot, and human-in-the-loop preference modeling.Cyber-physical systems have significantly changed how human society functions. In the article authored by Gupta et al., entitled “On Designing Evolving Cyber-Physical-Social Systems: A Decision-Based Design Perspective,” design principles for next-generation cyber-physical-social systems with decision-centric, socially cognizant, self-organizing, and continuously evolving capabilities are proposed. Research questions related to design challenges in the social space are raised, including modeling, quantification, and predictions of emotion, behavior, trust, coordination, and judgment. Research opportunities to enable the co-evolution and convergence of cyber, physical, and social spaces and to incorporate cognitive and ethical transformation are also postulated. In the article entitled “Positioning Multi-Intelligence Agents for Healthcare Enterprises: Toward Evolving Cyber-Physical-Social Systems,” Milisavljevic-Syed emphasized the importance of designing-for-service approach when engineering the future cyber-physical-social systems such as those for healthcare infrastructure. Particularly, a mental model capable of self-adaptation, sociocultural learning, and empathy, which can be embodied in multi-intelligence agent decisions is highlighted. Additional needs for future research for human-aligned systems, including patient privacy, safety, and ethical accountability, are proposed. Another important system engineering concept is the digital twin. In the article entitled “Differentiating Between Digital Twins and Control Systems for Complex Systems,” Lussier et al. provided reflections about the traditional and new roles of control systems in the context of digital twins. As a digital replicate of a physical or social system, a digital twin can be dynamically updated based on the data from the evolving physical twin and, at the same time, contribute to the evolution with input and feedback. In systems design, control systems would need to be treated as a part of physical twins.In summary, computing and information science has been pivotal for technoeconomic advancement in the past decades. Engineering research communities will continue to embrace the continuously evolving computational technologies. Not only will these technologies enhance engineering productivity and automation, but they also change the roles of engineers in the practice of design, manufacturing, and operation in engineering enterprises. The skill sets for developing and utilizing computational tools are essential for future engineers. JCISE will continue to play a major role in transforming the landscape of engineering research and engineering education. | |
