Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Rahman, MD Ashiqur"'
Autor:
Mehereen, Taskin, Chanda, Shorup, Nitu, Afrina Ayrin, Jami, Jubaer Tanjil, Rahim, Rafia Rizwana, Rahman, Md Ashiqur
Although real surfaces exhibit intricate topologies at the nanoscale, rough surface consideration is often overlooked in nanoscale heat transfer studies. Superimposed sinusoidal functions effectively model the complexity of these surfaces. This study
Externí odkaz:
http://arxiv.org/abs/2411.10820
In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identi
Externí odkaz:
http://arxiv.org/abs/2404.01501
Autor:
Rahman, Md Ashiqur, George, Robert Joseph, Elleithy, Mogab, Leibovici, Daniel, Li, Zongyi, Bonev, Boris, White, Colin, Berner, Julius, Yeh, Raymond A., Kossaifi, Jean, Azizzadenesheli, Kamyar, Anandkumar, Anima
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions between physical variables, and the limited amounts of high-resolutio
Externí odkaz:
http://arxiv.org/abs/2403.12553
Autor:
Rahman, Md Ashiqur, Yeh, Raymond A.
In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance. Recent works have made progress in developing scale-equivariant convolutio
Externí odkaz:
http://arxiv.org/abs/2311.02922
Numerous fires break out, especially from January to March every year, destroying thousands of shelters in the Rohingya Refugee Camps. In this study, a computational approach has been taken to analyze the fire dynamic behavior of informal settlements
Externí odkaz:
http://arxiv.org/abs/2310.06078
Autor:
Viswanath, Hrishikesh, Rahman, Md Ashiqur, Vyas, Abhijeet, Shor, Andrey, Medeiros, Beatriz, Hernandez, Stephanie, Prameela, Suhas Eswarappa, Bera, Aniket
Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering and mathematical problems involving functions of several variables, such as the propagation of heat or sound, f
Externí odkaz:
http://arxiv.org/abs/2301.13331
Autor:
Rahman, Md Ashiqur, Ghosh, Jasorsi, Viswanath, Hrishikesh, Azizzadenesheli, Kamyar, Bera, Aniket
We address the problem of generating 3D human motions in dyadic activities. In contrast to the concurrent works, which mainly focus on generating the motion of a single actor from the textual description, we generate the motion of one of the actors f
Externí odkaz:
http://arxiv.org/abs/2211.16210
We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and suffer from not b
Externí odkaz:
http://arxiv.org/abs/2211.10791
Autor:
Rahman, Md Ashiqur, Florez, Manuel A., Anandkumar, Anima, Ross, Zachary E., Azizzadenesheli, Kamyar
We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled f
Externí odkaz:
http://arxiv.org/abs/2205.03017
Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel methods to learn such maps and demonstrated unprecedented success in
Externí odkaz:
http://arxiv.org/abs/2204.11127