197 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of ViennaAT19 papers
- TU WienAT12 papers
- Wolfgang Pauli InstituteAT9 papers
- University of Applied Sciences St PöltenAT8 papers
- Toyota Motor Corporation (Japan)JP6 papers
- Christian Doppler Laboratory for ThermoelectricityAT5 papers
- University of ExeterGB4 papers
- Austrian Center for Medical Innovation and TechnologyAT3 papers
- Danube Private UniversityAT3 papers
- Infineon Technologies (Austria)AT3 papers
- Technische Universität BerlinDE3 papers
- Universidad de BurgosES3 papers
56 papers
Digital Twins for Small Towns and Rural Regions: Data Integration, Simulation and Visualisation Across Three Use Cases in Lower Austria
Thomas Delissen, Lukas Daniel Klausner, Maximilian Lackner +2
Smart city and digital twin concepts hold considerable potential for improving local governance and planning, yet practical implementations have almost exclusively focused on large…
Chasing Meaning and/or Insight? A Survey on Evaluation Practices at the Intersection of Visualization and the Humanities
Alejandro Benito-Santos, Florian Windhager, Aida Horaniet Ibañez +3
The intersection of visualization and the humanities (VIS*H) is marked by a tension between chasing analytical "insight" and interpretive "meaning." The effectiveness of visualizat…
From ACR O-RADS 2022 to Explainable Deep Learning: Comparative Performance of Expert Radiologists, Convolutional Neural Networks, Vision Transformers, and Fusion Models in Ovarian Masses
Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi +6
Background: The 2022 update of the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound classification refines risk stratification for adnexal lesions, yet human interpret…
Small Towns, Big Questions: Methodological Insights into Use Case Selection for Digital Twins in Small Towns
Lucy Temple, Gabriela Viale Pereira, Laura Kaltenbrunner +1
Selecting appropriate use cases for implementing digital solutions in small towns is a recurring challenge for smart city projects. This paper presents a transdisciplinary methodol…
SOT Enabled 3D Magnetic Field Sensor with Low Offset and High Sensitivity
Sebastian Zeilinger, Johannes Güttinger, Klemens Prügl +10
In this work we demonstrate a spin-orbit torque (SOT) magnetic field sensor, designed as a Ta/CoFeB/MgO structure, with high sensitivity and capable of active offset compensation i…
Modeling liquid-mediated interactions for close-to-substrate magnetic microparticle transport in dynamic magnetic field landscapes
Markus Gusenbauer, Rico Huhnstock, Alexander Kovacs +3
Understanding the on-chip motion of magnetic particles in a microfluidic environment is key to realizing magnetic particle-based Lab-on-a-chip systems for medical diagnostics. In t…
NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics
Claas Abert, Florian Bruckner, Andrey Voronov +11
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTo…
Remote-sensing based control of 3D magnetic fields using machine learning for in-operando applications
Miguel A. Cascales Sandoval, J. Jurczyk, L. Skoric +6
In-operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of mate…
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval
Amirreza Mahbod, Nematollah Saeidi, Sepideh Hatamikia +1
Medical image retrieval refers to the task of finding similar images for given query images in a database, with applications such as diagnosis support. While traditional medical im…
DIDChain: Advancing Supply Chain Data Management with Decentralized Identifiers and Blockchain
Patrick Herbke, Sid Lamichhane, Kaustabh Barman +4
Supply chain data management faces challenges in traceability, transparency, and trust. These issues stem from data silos and communication barriers. This research introduces DIDCh…
Unravelling the Use of Digital Twins to Assist Decision- and Policy-Making in Smart Cities
Lucy Temple, Gabriela Viale Pereira, Lukas Daniel Klausner
This short paper represents a systematic literature review that sets the basis for the future development of a framework for digital twin-based decision support in the public secto…
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Nematollah Saeidi, Hossein Karshenas, Bijan Shoushtarian +3
Breast cancer is the most common cancer type in women worldwide. Early detection and appropriate treatment can significantly reduce its impact. While histopathology examinations pl…
Forms of Understanding for XAI-Explanations
Hendrik Buschmeier, Heike M. Buhl, Friederike Kern +17
Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of pr…
Smart Cities and Digital Twins in Lower Austria
Gabriela Viale Pereira, Lukas Daniel Klausner, Lucy Temple +3
Smart city solutions require innovative governance approaches together with the smart use of technology, such as digital twins, by city managers and policymakers to manage the big…
A Model Based Framework for Testing Safety and Security in Operational Technology Environments
Mukund Bhole, Wolfgang Kastner, Thilo Sauter
Todays industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, al…
Towards a Success Model for Automated Programming Assessment Systems Used as a Formative Assessment Tool
Clemens Sauerwein, Tobias Antensteiner, Stefan Oppl +4
The assessment of source code in university education is a central and important task for lecturers of programming courses. In doing so, educators are confronted with growing numbe…
Visualizing Uncertainty in Sets
Christian Tominski, Michael Behrisch, Susanne Bleisch +4
Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data is uncertain is still an open research challenge.…
Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization
Sebastian Schaffer, Thomas Schrefl, Harald Oezelt +5
We study the full 3d static micromagnetic equations via a physics-informed neural network (PINN) ansatz for the continuous magnetization configuration. PINNs are inherently mesh-fr…
Multi-label Classification of Aircraft Heading Changes Using Neural Network to Resolve Conflicts
Md Siddiqur Rahman, Laurent Lapasset, Josiane Mothe
An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Specific air traffic controllers are assigned to solve such conflicts. A control…
Proposal for a micromagnetic standard problem: domain wall pinning at phase boundaries
Paul Heistracher, Claas Abert, Florian Bruckner +2
We propose a novel micromagnetic standard problem calculating the coercive field for unpinning a domain wall at the interface of a multiphase magnet. This problem is sensitive to d…
Magnetostatics and micromagnetics with physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +8
Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimiz…
Conditional physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +11
We introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. The concept of PINNs is expanded to learn not only…
Machine learning methods for the prediction of micromagnetic magnetization dynamics
Sebastian Schaffer, Norbert J. Mauser, Thomas Schrefl +2
Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-de…
Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method
Lukas Exl, Norbert J. Mauser, Sebastian Schaffer +2
We establish a machine learning model for the prediction of the magnetization dynamics as function of the external field described by the Landau-Lifschitz-Gilbert equation, the par…