Correction: Meyer zu Westerhausen et al. Optimisation of Sensor and Sensor Node Positions for Shape Sensing with a Wireless Sensor Network—A Case Study Using the Modal Method and a Physics-Informed Neural Network. Sensors 2025, 25, 5573 [PDF]
Sensors (Basel)Sören Meyer zu Westerhausen +3 more
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Optimisation of Sensor and Sensor Node Positions for Shape Sensing with a Wireless Sensor Network—A Case Study Using the Modal Method and a Physics-Informed Neural Network [PDF]
Sensors (Basel)Sören Meyer zu Westerhausen +3 more
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Graph Coloring with Physics-Inspired Graph Neural Networks [PDF]
Physical Review Research, 2022 We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multi-class node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model ...
M. Schuetz +3 more
semanticscholar +1 more source
An electrostatics method for converting a time-series into a weighted complex network
Scientific Reports, 2021 This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles ...
Dimitrios Tsiotas +2 more
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Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors
Electronics, 2022 In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generation nano-device.
SangMin Woo +5 more
semanticscholar +1 more source
Evaluation of HPC Acceleration and Interconnect Technologies for High-Throughput Data Acquisition
Sensors, 2021 Efficient data movement in multi-node systems is a crucial issue at the crossroads of scientific computing, big data, and high-performance computing, impacting demanding data acquisition applications from high-energy physics to astronomy, where dedicated
Alessandro Cilardo
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Neural-prior stochastic block model
Machine Learning: Science and Technology, 2023 The stochastic block model (SBM) is widely studied as a benchmark for graph clustering aka community detection. In practice, graph data often come with node attributes that bear additional information about the communities.
O Duranthon, L Zdeborová
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Benchmarks for physics-informed data-driven hyperelasticity [PDF]
arXiv.org, 2023 Data-driven methods have changed the way we understand and model materials. However, while providing unmatched flexibility, these methods have limitations such as reduced capacity to extrapolate, overfitting, and violation of physics constraints.
Vahidullah Tac +4 more
semanticscholar +1 more source
Materials Research Express, 2020 We report a distinctly different zero feld cooled negative exchange bias(EB) effect in Ni doped LaFeO _3 nanoparticle where the EB field is both temperature and concentration dependent.
T Lakshmana Rao +3 more
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Coherent backscattering in the topological Hall effect
Materials for Quantum Technology, 2023 The mutual interplay between electron transport and magnetism has attracted considerable attention in recent years, primarily motivated by strategies to manipulate magnetic degrees of freedom electrically, such as spin–orbit torques and domain wall ...
Hong Liu +3 more
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