Zobrazeno 1 - 10
of 1 719
pro vyhledávání: '"P. Fotiadis"'
Autor:
Liu, Xinjie, Li, Jingqi, Fotiadis, Filippos, Karabag, Mustafa O., Milzman, Jesse, Fridovich-Keil, David, Topcu, Ufuk
Common feedback strategies in multi-agent dynamic games require all players' state information to compute control strategies. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or
Externí odkaz:
http://arxiv.org/abs/2410.16441
Autor:
Fotiadis, Stathi, Brenowitz, Noah, Geffner, Tomas, Cohen, Yair, Pritchard, Michael, Vahdat, Arash, Mardani, Morteza
Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolving small-scale details poses significant challenges due to: (i) misalig
Externí odkaz:
http://arxiv.org/abs/2410.19814
Autor:
Ashton, Neil, Mockett, Charles, Fuchs, Marian, Fliessbach, Louis, Hetmann, Hendrik, Knacke, Thilo, Schonwald, Norbert, Skaperdas, Vangelis, Fotiadis, Grigoris, Walle, Astrid, Hupertz, Burkhard, Maddix, Danielle
Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fide
Externí odkaz:
http://arxiv.org/abs/2408.11969
This work introduces EffiSegNet, a novel segmentation framework leveraging transfer learning with a pre-trained Convolutional Neural Network (CNN) classifier as its backbone. Deviating from traditional architectures with a symmetric U-shape, EffiSegN
Externí odkaz:
http://arxiv.org/abs/2407.16298
Autor:
Nguyen-Fotiadis, Nga T. T., Chiodi, Robert, McKerns, Michael, Livescu, Daniel, Sornborger, Andrew
The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical di
Externí odkaz:
http://arxiv.org/abs/2405.08185
Autor:
Bhalerao, Omkar, Suckow, Stephan, Windgassen, Horst, Biller, Harry, Fotiadis, Konstantinos, Simos, Stelios, Chatzianagnostou, Evangelia, Spasopoulos, Dimosthenis, Das, Pratyusha, Markey, Laurent, Weeber, Jean-Claude, Pleros, Nikos, Schirmer, Matthias, Lemme, Max C.
Plasmonic refractive index sensors are essential for detecting subtle variations in the ambient environment through surface plasmon interactions. Current efforts utilizing CMOS-compatible, plasmo-photonic Mach-Zehnder interferometers with active powe
Externí odkaz:
http://arxiv.org/abs/2405.05716
Autor:
Niu, Shengyuan, Bouland, Ali, Wang, Haoran, Fotiadis, Filippos, Kurdila, Andrew, L'Afflitto, Andrea, Paruchuri, Sai Tej, Vamvoudakis, Kyriakos G.
In this paper, the evolution equation that defines the online critic for the approximation of the optimal value function is cast in a general class of reproducing kernel Hilbert spaces (RKHSs). Exploiting some core tools of RKHS theory, this formulat
Externí odkaz:
http://arxiv.org/abs/2405.05887
Autor:
Xia, Guoxuan, Danier, Duolikun, Das, Ayan, Fotiadis, Stathi, Nabiei, Farhang, Sengupta, Ushnish, Bernacchia, Alberto
Recently, Zhang et al. have proposed the Diffusion Exponential Integrator Sampler (DEIS) for fast generation of samples from Diffusion Models. It leverages the semi-linear nature of the probability flow ordinary differential equation (ODE) in order t
Externí odkaz:
http://arxiv.org/abs/2311.00157
Autor:
Vasileios C. Pezoulas, Dimitrios I. Zaridis, Eugenia Mylona, Christos Androutsos, Kosmas Apostolidis, Nikolaos S. Tachos, Dimitrios I. Fotiadis
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2892-2910 (2024)
Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with
Externí odkaz:
https://doaj.org/article/1595553cc13b4b048a4661b05bb4ccd8
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2152-2162 (2024)
Background and objective: Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms ha
Externí odkaz:
https://doaj.org/article/87e649650bbf40ff947cc67f752506d8