Zobrazeno 1 - 10
of 27 414
pro vyhledávání: '"LI Can"'
Autor:
Mollaali, Amirhossein, Zufferey, Gabriel, Constante-Flores, Gonzalo, Moya, Christian, Li, Can, Lin, Guang, Yue, Meng
This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems. We begin by introducing the Quantile Attention-Fourier Deep Operator Network (QAF-DeepONet), designed to capture the compl
Externí odkaz:
http://arxiv.org/abs/2410.24162
In scenarios with limited training data or where explainability is crucial, conventional neural network-based machine learning models often face challenges. In contrast, Bayesian inference-based algorithms excel in providing interpretable predictions
Externí odkaz:
http://arxiv.org/abs/2410.19356
This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based but simple representation provided by th
Externí odkaz:
http://arxiv.org/abs/2410.18413
Autor:
Zhang, Hanlei, Bai, Jincheng, Chen, Xiabo, Li, Can, Zhong, Chuanjian, Fang, Jiye, Zhou, Guangwen
Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level)
Externí odkaz:
http://arxiv.org/abs/2409.16637
Spin-1/2 Heisenberg antiferromagnetic chains are excellent one-dimensional platforms for exploring quantum magnetic states and quasiparticle fractionalization. Understanding its quantum magnetism and quasiparticle excitation at the atomic scale is cr
Externí odkaz:
http://arxiv.org/abs/2408.08801
Autor:
Peng, Zedong, Cao, Kaiyu, Furman, Kevin C., Li, Can, Grossmann, Ignacio E., Neira, David E. Bernal
The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer- Appro
Externí odkaz:
http://arxiv.org/abs/2407.20973
Autor:
Ramanujam, Asha, Li, Can
Optimization models with decision variables in multiple time scales are widely used across various fields such as integrated planning and scheduling. To address scalability challenges in these models, we present the Parametric Autotuning Multi-Time S
Externí odkaz:
http://arxiv.org/abs/2407.16570
Autor:
Liu, Yufeng, Gu, Yu, Bao, Ting, Mao, Ning, Li, Can, Jiang, Shudan, Liu, Liang, Guan, Dandan, Li, Yaoyi, Zheng, Hao, Liu, Canhua, Watanabe, Kenji, Taniguchi, Takashi, Duan, Wenhui, Jia, Jinfeng, Liu, Xiaoxue, Zhang, Yang, Li, Tingxin, Wang, Shiyong
Two-dimensional (2D) moir\'e materials have emerged as a highly tunable platform for investigating novel quantum states of matter arising from strong electronic correlations and nontrivial band topology. Recently, topological flat bands formed in 2D
Externí odkaz:
http://arxiv.org/abs/2406.19310
Ultra-Long Homochiral Graphene Nanoribbons Grown Within h-BN Stacks for High-Performance Electronics
Autor:
Lyu, Bosai, Chen, Jiajun, Wang, Sen, Lou, Shuo, Shen, Peiyue, Xie, Jingxu, Qiu, Lu, Mitchell, Izaac, Li, Can, Hu, Cheng, Zhou, Xianliang, Watanabe, Kenji, Taniguchi, Takashi, Wang, Xiaoqun, Jia, Jinfeng, Liang, Qi, Chen, Guorui, Li, Tingxin, Wang, Shiyong, Ouyang, Wengen, Hod, Oded, Ding, Feng, Urbakh, Michael, Shi, Zhiwen
Van der Waals encapsulation of two-dimensional materials within hexagonal boron nitride (h-BN) stacks has proven to be a promising way to create ultrahigh-performance electronic devices. However, contemporary approaches for achieving van der Waals en
Externí odkaz:
http://arxiv.org/abs/2403.11465
When faced with a limited budget of function evaluations, state-of-the-art black-box optimization (BBO) solvers struggle to obtain globally, or sometimes even locally, optimal solutions. In such cases, one may pursue solution polishing, i.e., a compu
Externí odkaz:
http://arxiv.org/abs/2402.12283