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Scalable and efficient numerical simulations continue to gain importance, as computation is firmly established as the third pillar of discovery, alongside theory and experiment. Meanwhile, the performance of computing hardware grows through…
Quantum embedding methods enable the study of large, strongly correlated quantum systems by (usually self-consistent) decomposition into computationally manageable subproblems, in the spirit of divide-and-conquer methods. Among these,…
Common and unique features of nuclear physics measurements are examined. Such analysis with respect to existing hardware and software platforms and standards allows to algorithmize the DAQ, monitoring and processing tasks. A universal…
In this communication the advantages and drawbacks of the isogeometric analysis (IGA) are reviewed in the context of electromagnetic simulations. IGA extends the set of polynomial basis functions, commonly employed by the classical Finite…
The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies explored learning-based techniques for cross-stage prediction…
Quantitative microstructural characterization is fundamental to materials science, where electron micrograph (EM) provides indispensable high-resolution insights. However, progress in deep learning-based EM characterization has been…
The Open Knowledgebase of Interatomic Models (OpenKIM) project is a framework intended to facilitate access to standardized implementations of interatomic models for molecular simulations along with computational protocols to evaluate them.…
Minor embedding is a required compilation step for quantum annealing, mapping logical problem graphs onto sparse hardware topologies. Despite its central role in determining solution quality, no standardized benchmark exists for comparing…
This paper presents a methodology and software tools for parametric design of complex architectural objects, called digital or algorithmic forms. In order to provide a flexible tool, the proposed design philosophy involves two open source…
This article aims at facilitating the widespread application of Energy Management Systems (EMSs), especially on buildings and cities, in order to support the realization of future carbon-neutral energy systems. We claim that economic…
Traditional massive multiple-input multiple-output (MIMO) information theory adopt non-physically consistent assumptions, including white-noised, scalar-quantity, far-field, discretized, and monochromatic EM fields, which mismatch the…
Energy system models have become indispensable tools for planning future energy systems by providing insights into different development trajectories. However, sustainable systems with high shares of renewable energy are characterized by…
Entity Matching (EM)--the task of determining whether two data records refer to the same real-world entity--is a core task in data integration. Recent advances in deep learning have set a new standard for EM, particularly through…
According to the FAIR (findability, accessibility, interoperability, and reusability) principles, scientific data should always be stored with machine-readable descriptive metadata. Existing solutions to store data with metadata, such as…
The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of experimental data, the MEM does not rely on an…
ComDMFT is a massively parallel computational package to study the electronic structure of correlated-electron systems (CES). Our approach is a parameter-free method based on ab initio linearized quasiparticle self-consistent GW (LQSGW) and…
As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is…
The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of…
MEG (Mu to Electron Gamma) is an experiment dedicated to search for the $\mu^+ \rightarrow e^+\gamma$ decay that is strongly suppressed in the Standard Model but predicted in several Super Symmetric extensions of it at an accessible rate.…
Time- and frequency resolved optical signals provide insights into the properties of light harvesting molecular complexes, including excitation energies, dipole strengths and orientations, as well as in the exciton energy flow through the…