Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Chandramowlishwaran P"'
Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addres
Externí odkaz:
http://arxiv.org/abs/2309.01025
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains. Its unique approach leverages pre-trained networks on small domains to solve partial differential equations on large domains pu
Externí odkaz:
http://arxiv.org/abs/2308.14258
Autor:
Hassan, Sheikh Md Shakeel, Feeney, Arthur, Dhruv, Akash, Kim, Jihoon, Suh, Youngjoon, Ryu, Jaiyoung, Won, Yoonjin, Chandramowlishwaran, Aparna
In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse g
Externí odkaz:
http://arxiv.org/abs/2307.14623
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not be
Externí odkaz:
https://doaj.org/article/875334722b054897b8c4fd9d1d88f257
Hybrid MPI+threads programming is gaining prominence, but, in practice, applications perform slower with it compared to the MPI everywhere model. The most critical challenge to the parallel efficiency of MPI+threads applications is slow MPI_THREAD_MU
Externí odkaz:
http://arxiv.org/abs/2206.14285
Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of high-resolution turbulent flows (aka super-resolution). However, current DL approaches perform spatially uniform super-resolution - a key performance limiter fo
Externí odkaz:
http://arxiv.org/abs/2203.14154
Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolution training data to learn accurate models. The size and availability
Externí odkaz:
http://arxiv.org/abs/2108.07667
Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of-the-art PINNs have many attractive features, they approximate a speci
Externí odkaz:
http://arxiv.org/abs/2104.10873
GPUs are currently the platform of choice for training neural networks. However, training a deep neural network (DNN) is a time-consuming process even on GPUs because of the massive number of parameters that have to be learned. As a result, accelerat
Externí odkaz:
http://arxiv.org/abs/2005.13823
CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a motor vehicle. However, many systems of interest are prohibitively expens
Externí odkaz:
http://arxiv.org/abs/2005.04485