Related papers: MESS: Modern Electronic Structure Simulations
Structural Equation Models (SEMs) are routinely used in the analysis of empirical data by researchers from different scientific fields such as psychologists or economists. In some fields, such as in ecology, SEMs have only started recently…
We review various changes made in recent years to the software development process of the LAMMPS simulation software package and the software itself. We discuss how those changes have impacted the effort and workflow required to develop and…
Mathematics has many useful properties for developing of complex software systems. One is that it can exactly describe a physical situation of the object or outcome of an action. Mathematics support abstraction and this is an excellent…
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Scientific ML that are easy to use but still challenging and…
Nowadays, we are witnessing the advent of the Internet of Things (EC) with numerous devices performing interactions between them or with end users. The huge number of devices leads to huge volumes of collected data that demand the…
We introduce $\textbf{MASSE}$, the first Multi-Agent System for Structural Engineering, effectively integrating large language model (LLM)-based agents with real-world engineering workflows. Structural engineering is a fundamental yet…
Machine learning (ML) - based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E)…
Many approaches in the field of machine learning and data analysis rely on the assumption that the observed data lies on lower-dimensional manifolds. This assumption has been verified empirically for many real data sets. To make use of this…
A major step in developing robust wireless systems is to test and validate the design under a variety of circumstances. As wireless networks become more complex, it is impractical to perform testing on a real deployment. As a result, the…
Modeling and Simulation (M&S) for system design and prototyping is practiced today both in the industry and academia. M&S are two different areas altogether and have specific objectives. However, most of the times these two separate areas…
Inserting renewable energy in the electric grid in a decentralized manneris a key challenge of the energy transition. However, at local scale, both production and demand display erratic behavior, which makes it delicate to match them. It is…
In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essentially a low-code metaverse authoring platform for…
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex…
Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering,…
Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with "sparks of intelligence". However, their runtime requirements have prevented them from being…
Model-Based Systems Engineering (MBSE) is a development approach aiming to build correct-by-construction systems, provided the use of clear, unambiguous and complete models to describe them along the design process. The approach is…
This paper introduces prompted software engineering (PSE), which integrates prompt engineering to build effective prompts for language-based AI models, to enhance the software development process. PSE enables the use of AI models in…
Continuous embeddings of tokens in computer programs have been used to support a variety of software development tools, including readability, code search, and program repair. Contextual embeddings are common in natural language processing…
Automated Essay Scoring (AES) is a cross-disciplinary effort involving Education, Linguistics, and Natural Language Processing (NLP). The efficacy of an NLP model in AES tests it ability to evaluate long-term dependencies and extrapolate…
The aim of the FESCA workshop is to bring together junior researchers from formal methods, software engineering, and industry interested in the development and application of formal modelling approaches as well as associated analysis and…