Zobrazeno 1 - 10
of 7 975
pro vyhledávání: '"Mukerji A"'
Autor:
Chung, Jaehong, Marcato, Agnese, Guiltinan, Eric J., Mukerji, Tapan, Viswanathan, Hari, Lin, Yen Ting, Santos, Javier E.
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of in
Externí odkaz:
http://arxiv.org/abs/2406.19333
Autor:
Kashefi, Ali, Mukerji, Tapan
Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification of network architectures, in contrast to traditional convolutional neu
Externí odkaz:
http://arxiv.org/abs/2402.11568
Autor:
Chung, Jaehong, Marcato, Agnese, Guiltinan, Eric J., Mukerji, Tapan, Lin, Yen Ting, Santos, Javier E.
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen storage efficiency and forecasting CO$_2$ sequestration processes in u
Externí odkaz:
http://arxiv.org/abs/2312.04375
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorithm to transform 3D digital rock images into graph datasets, encapsulati
Externí odkaz:
http://arxiv.org/abs/2310.19274
Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic behavior, however, incurs high computational cost; and surrogate machine
Externí odkaz:
http://arxiv.org/abs/2305.06519
Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand, fully supervised learning models are first trained usually over a few
Externí odkaz:
http://arxiv.org/abs/2303.13634
Autor:
Kashefi, Ali, Mukerji, Tapan
ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numerical algorithms. Specifically, we examine the capability of GhatGPT for
Externí odkaz:
http://arxiv.org/abs/2303.12093
Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurf
Externí odkaz:
http://arxiv.org/abs/2302.09107
Publikováno v:
Canadian Journal of Respiratory Therapy, Vol 52, Iss 3 (2016)
OBJECTIVE To evaluate the impact and effectiveness of an experiential interprofessional education workshop on noninvasive ventilation (NIV) in the setting of a neonatal intensive care unit. METHODS In the present cross-sectional study, a full-day
Externí odkaz:
https://doaj.org/article/dcf49a1586e74660b4e5423958de85c7
Autor:
Mukerji, Tantrik, Yang, Wei-Shih
We present a randomized algorithm for estimating the permanent of an $M \times M$ real matrix $A$ up to an additive error. We do this by viewing the permanent $\mathrm{perm}(A)$ of $A$ as the expectation of a product of centered joint Gaussian random
Externí odkaz:
http://arxiv.org/abs/2212.10672