Zobrazeno 1 - 10
of 2 292
pro vyhledávání: '"A. Yousefpour"'
Publikováno v:
Advanced Manufacturing: Polymer & Composites Science, Vol 8, Iss 2, Pp 68-96 (2022)
An extensive review of literature is conducted to present the evolution of the field of repair of thermoplastic composites (TPC’s) from when it was first mentioned in 1980. The TPC materials used today in aerospace structures are introduced along w
Externí odkaz:
https://doaj.org/article/42f19fbb1fc94f8f97ecfbed883a5beb
Existing fairness benchmarks for large language models (LLMs) primarily focus on simple tasks, such as multiple-choice questions, overlooking biases that may arise in more complex scenarios like long-text generation. To address this gap, we introduce
Externí odkaz:
http://arxiv.org/abs/2410.17519
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
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