Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Duraisamy, Karthik"'
Autor:
Zambrano, Noah, Duraisamy, Karthik
A stochastic wavevector approach is formulated to accurately represent compressible turbulence subject to rapid deformations. This approach is inspired by the incompressible particle representation model of Kassinos (1995) and preserves the exact nat
Externí odkaz:
http://arxiv.org/abs/2409.12791
Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of noisy or incomplete data. In this study, we develop and enhance score
Externí odkaz:
http://arxiv.org/abs/2409.00230
Autor:
Raje, Pratikkumar, Duraisamy, Karthik
A wall-modeled large eddy simulation approach is proposed in a Discontinuous Galerkin (DG) setting, building on the slip-wall concept of Bae et al. (JFM'19) and the universal scaling relationship by Pradhan and Duraisamy (JFM'23). The effect of the o
Externí odkaz:
http://arxiv.org/abs/2405.15899
Autor:
Duraisamy, Karthik
Recent studies show that transformer-based architectures emulate gradient descent during a forward pass, contributing to in-context learning capabilities - an ability where the model adapts to new tasks based on a sequence of prompt examples without
Externí odkaz:
http://arxiv.org/abs/2405.02462
Autor:
Bhola, Sahil, Duraisamy, Karthik
Advancements in computer hardware have made it possible to utilize low- and mixed-precision arithmetic for enhanced computational efficiency. In practical predictive modeling, however, it is vital to quantify uncertainty due to rounding along other s
Externí odkaz:
http://arxiv.org/abs/2404.12556
Autor:
Dong, Jiayuan, Jacobsen, Christian, Khalloufi, Mehdi, Akram, Maryam, Liu, Wanjiao, Duraisamy, Karthik, Huan, Xun
Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood.
Externí odkaz:
http://arxiv.org/abs/2404.13056
Recent advances in generative artificial intelligence have had a significant impact on diverse domains spanning computer vision, natural language processing, and drug discovery. This work extends the reach of generative models into physical problem d
Externí odkaz:
http://arxiv.org/abs/2312.10527
Autor:
Uchida, Daisuke, Duraisamy, Karthik
The Koopman operator framework provides a perspective that non-linear dynamics can be described through the lens of linear operators acting on function spaces. As the framework naturally yields linear embedding models, there have been extensive effor
Externí odkaz:
http://arxiv.org/abs/2308.13051
Autor:
Sanchis-Agudo, Marcial, Wang, Yuning, Arnau, Roger, Guastoni, Luca, Lim, Jasmin, Duraisamy, Karthik, Vinuesa, Ricardo
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction. While t
Externí odkaz:
http://arxiv.org/abs/2308.12874
Autor:
Rezaian, Elnaz, Duraisamy, Karthik
Rapid prediction of the aeroacoustic response is a key component in the design of aircraft and turbomachinery. While it is possible to achieve accurate predictions using direct solution of the compressible Navier-Stokes equations, applications of suc
Externí odkaz:
http://arxiv.org/abs/2304.13900