Zobrazeno 1 - 10
of 726
pro vyhledávání: '"Yousefpour P"'
Operator learning focuses on approximating mappings $\mathcal{G}^\dagger:\mathcal{U} \rightarrow\mathcal{V}$ between infinite-dimensional spaces of functions, such as $u: \Omega_u\rightarrow\mathbb{R}$ and $v: \Omega_v\rightarrow\mathbb{R}$. This mak
Externí odkaz:
http://arxiv.org/abs/2409.04538
Topology optimization (TO) provides a principled mathematical approach for optimizing the performance of a structure by designing its material spatial distribution in a pre-defined domain and subject to a set of constraints. The majority of existing
Externí odkaz:
http://arxiv.org/abs/2408.03490
Publikováno v:
International Journal of Nanomedicine, Vol Volume 17, Pp 5047-5048 (2022)
Yousefpour P, Atyabi F, Vasheghani-Farahani E, Movahedi M, Dinarvand R. Int J Nanomedicine. 2011;6:1977—1990. The authors have advised Figure 9D on page 1986 is incorrect. Due to an error at the time of figure assembly Figure 9B and D were duplicat
Externí odkaz:
https://doaj.org/article/3a7d260cff014de1a4608ce52117635c
Autor:
Yousefpour, Negin, Wang, Bo
This paper introduces scour physics-inspired neural networks (SPINNs), a hybrid physics-data-driven framework for bridge scour prediction using deep learning. SPINNs integrate physics-based, empirical equations into deep neural networks and are train
Externí odkaz:
http://arxiv.org/abs/2407.01258
Autor:
Chung, Jiwan, Lee, Sungjae, Kim, Minseo, Han, Seungju, Yousefpour, Ashkan, Hessel, Jack, Yu, Youngjae
Visual arguments, often used in advertising or social causes, rely on images to persuade viewers to do or believe something. Understanding these arguments requires selective vision: only specific visual stimuli within an image are relevant to the arg
Externí odkaz:
http://arxiv.org/abs/2406.18925
Autor:
Hashem, Tahrima, Yousefpour, Negin
Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions.
Externí odkaz:
http://arxiv.org/abs/2404.16549
Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we present a novel alignment framework, SELF-JUDGE that (1) does on-polic
Externí odkaz:
http://arxiv.org/abs/2402.11253
Physics-informed machine learning (PIML) has emerged as a promising alternative to conventional numerical methods for solving partial differential equations (PDEs). PIML models are increasingly built via deep neural networks (NNs) whose architecture
Externí odkaz:
http://arxiv.org/abs/2401.03492
In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on P
Externí odkaz:
http://arxiv.org/abs/2312.07694
Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acquiring high-fidelity (HF) data through physical experiments or HF simul
Externí odkaz:
http://arxiv.org/abs/2309.02771