23 papers · ranked by Valyu relevance
Robert France, Bernhard Rumpe⋆
Robert France is a Professor in the Department of Computer Science at Colorado State University. His research focuses on the problems associated with the development of complex software systems. He is involved in research on rigorous software modeling, on providing rigorous support for using design patterns, and on…
Hira Naveed, Chetan Arora, Hourieh Khalajzadeh, John Grundy + 1 more
'Omar Haggag'] Context: Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to increasingly leverage their data to extract meaningful insights and enhance business profitability. ML components enable…
Zahra Mardani Korani, Armin Moin, Alberto Rodrigues da Silva, João Carlos Ferreira + 1 more
'João Carlos Ferreira' 'Raffaele Bruno'] This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In…
Loli Burgueño, Davide Di Ruscio, Houari Sahraoui, Manuel Wimmer
Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made on Artificial Intelligence (AI) techniques, questions arise for the future of MDE such as how existing…
Simon Raedler, Luca Berardinelli, Karolin Winter, Abbas Rahimi + 1 more
'Stefanie Rinderle‐Ma'] Background: Technical systems are becoming increasingly complex due to the increasing number of components, functions, and involvement of different disciplines. In this regard, Model-Driven Engineering techniques and practices tame complexity during the development process by using models as…
Hessa Alfraihi, Kevin Lano, Muhammad Suhail
In this study, we investigate the usability of Model-Driven Engineering (MDE) through interviews with fifteen practitioners from diverse roles (e.g., developers, researchers, architects) and domains, and with a range of expertise levels across academic and industrial software sectors, capturing in-depth perspectives on…
Hans Demski, Sebastian Garde, Claudia Hildebrand
Background Smart Health is known as a concept that enhances networking, intelligent data processing and combining patient data with other parameters. Open data models can play an important role in creating a framework for providing interoperable data services that support the development of innovative Smart Health…
María-José Escalona, Nora Koch, Laura Garcia-Borgoñon, Juan Lara
Background The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the…
Jean Bézivín, Richard F. Paige, Uwe Aßmann, Bernhard Rumpe⋆ + 1 more
Complex systems are hard to define [1]. Nevertheless they are more and more frequently encountered. Examples include a worldwide airline traffic management system, a global telecommunication or energy infrastructure or even the whole legacy portfolio accumulated for more than thirty years in a large insurance company.…
Göksel Mısırlı, Bill Yang, Katherine James, Anil Wipat
Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits where manual analysis is infeasible, permitting a model-driven design process. However, there are…
Hamid Bagheri
Developing complex software systems is costly, time-consuming and error-prone. Modeldriven development (MDD) promises to improve software productivity, timeliness, quality and cost through the transformation of abstract application models to code-level implementations. However, it remains unreasonably difficult to…
Diego Alonso, Francisco Sánchez-Ledesma, Pedro Sánchez, Juan A. Pastor + 1 more
'Juan A. Pastor' 'Bárbara Álvarez'] The use of frameworks and components has been shown to be effective in improving software productivity and quality. However, the results in terms of reuse and standardization show a dearth of portability either of designs or of component-based implementations. This paper, which is…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Atefe Darabi, Zheming An, Muhammad Ali Al-Radhawi, William Cho + 2 more
This work explores the integration of machine learning (ML) and mechanistic models (MM). While ML has demonstrated remarkable success in data-driven modeling across engineering, biology, and other scientific fields, MM remain essential for their interpretability and capacity to extrapolate beyond observed conditions…
Anna Matuszyńska, Oliver Ebenhöh, Matias D. Zurbriggen, Daniel C. Ducat + 1 more
Synthetic biology designs and constructs new biological parts, devices and systems with predetermined functionalities. With the unlimited ability to synthesise any DNA and RNA and transfer it to almost any organism, we are at the dawn of a new era in which biology is being recreated in ways never before possible. It…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Mahmoud Ibrahim, Viktor Rjabtšikov, Rolando Gilbert, Giambattista Gruosso
'Giambattista Gruosso'] Digital twin (DT) technology has been used in a wide range of applications, including electric vehicles. The DT platform provides a virtual representation or advanced simulation of a physical object in real-time. The implementation of DT on various aspects of EVs has recently transpired in…
Zhang Cheng, Avner Ronen, Heyang Yuan
Mechanistic models can provide predictive insight into the design and optimization of engineered biological systems, but the kinetic parameters in the models need to be frequently calibrated and uniquely identified. This limitation can be addressed by integrating mechanistic models with data-driven approaches, a…
Christopher Schölzel, Valeria Blesius, Gernot Ernst, Andreas Dominik
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an…
Alexander L.R. Lubbock, Carlos F. Lopez
Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin
A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor…
Authors not listed
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…