Zobrazeno 1 - 10
of 3 590
pro vyhledávání: '"Chatzi, A"'
Infrastructure systems are critical in modern communities but are highly susceptible to various natural and man-made disasters. Efficient post-disaster recovery requires repair-scheduling approaches under the limitation of capped resources that need
Externí odkaz:
http://arxiv.org/abs/2410.18577
The accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration control. Often, these models originate from physics-based principles and ca
Externí odkaz:
http://arxiv.org/abs/2410.01340
Autor:
Chatzi, Ivi, Benz, Nina Corvelo, Straitouri, Eleni, Tsirtsis, Stratis, Gomez-Rodriguez, Manuel
"Sure, I am happy to generate a story for you: Captain Lyra stood at the helm of her trusty ship, the Maelstrom's Fury, gazing out at the endless sea. [...] Lyra's eyes welled up with tears as she realized the bitter truth - she had sacrificed everyt
Externí odkaz:
http://arxiv.org/abs/2409.17027
Autor:
Vlachas, Konstantinos, Simpson, Thomas, Garland, Anthony, Quinn, D. Dane, Farhat, Charbel, Chatzi, Eleni
Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, s
Externí odkaz:
http://arxiv.org/abs/2407.17139
The Population-Based Structural Health Monitoring (PBSHM) paradigm has recently emerged as a promising approach to enhance data-driven assessment of engineering structures by facilitating transfer learning between structures with some degree of simil
Externí odkaz:
http://arxiv.org/abs/2407.06492
The task of open-set domain generalization (OSDG) involves recognizing novel classes within unseen domains, which becomes more challenging with multiple modalities as input. Existing works have only addressed unimodal OSDG within the meta-learning fr
Externí odkaz:
http://arxiv.org/abs/2407.01518
Detecting out-of-distribution (OOD) samples is important for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. Existing research has mainly focused on unimodal scenarios on image
Externí odkaz:
http://arxiv.org/abs/2405.17419
Large language models are often ranked according to their level of alignment with human preferences -- a model is better than other models if its outputs are more frequently preferred by humans. One of the popular ways to elicit human preferences uti
Externí odkaz:
http://arxiv.org/abs/2402.17826
Autor:
Mueller, Sebastian Achim, Daglas, Spyridon, Engels, Axel Arbet, Ahnen, Max Ludwig, Neise, Dominik, Egger, Adrian, Chatzi, Eleni, Biland, Adrian, Hofmann, Werner
Publikováno v:
Astroparticle Physics, Volume 158, Year 2024, 102933, ISSN 0927-6505, 2024.102933
Detecting cosmic gamma rays at high rates is the key to time-resolve the acceleration of particles within some of the most powerful events in the universe. Time-resolving the emission of gamma rays from merging celestial bodies, apparently random bur
Externí odkaz:
http://arxiv.org/abs/2401.16148
The automated discovery of constitutive laws forms an emerging research area, that focuses on automatically obtaining symbolic expressions describing the constitutive behavior of solid materials from experimental data. Existing symbolic/sparse regres
Externí odkaz:
http://arxiv.org/abs/2402.04263