Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Wu Tailin"'
Autor:
Wang, Haixin, Cao, Yadi, Huang, Zijie, Liu, Yuxuan, Hu, Peiyan, Luo, Xiao, Song, Zezheng, Zhao, Wanjia, Liu, Jilin, Sun, Jinan, Zhang, Shikun, Wei, Long, Wang, Yue, Wu, Tailin, Ma, Zhi-Ming, Sun, Yizhou
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then examine the va
Externí odkaz:
http://arxiv.org/abs/2408.12171
Autor:
Wei, Long, Feng, Haodong, Yang, Yuchen, Feng, Ruiqi, Hu, Peiyan, Zheng, Xiang, Zhang, Tao, Fan, Dixia, Wu, Tailin
The control problems of complex physical systems have broad applications in science and engineering. Previous studies have shown that generative control methods based on diffusion models offer significant advantages for solving these problems. Howeve
Externí odkaz:
http://arxiv.org/abs/2408.03124
Autor:
Wei, Long, Hu, Peiyan, Feng, Ruiqi, Feng, Haodong, Du, Yixuan, Zhang, Tao, Wang, Rui, Wang, Yue, Ma, Zhi-Ming, Wu, Tailin
Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computational costs. On the other hand, recent deep learning and reinforcement
Externí odkaz:
http://arxiv.org/abs/2407.06494
Nobel laureate Philip Anderson and Elihu Abrahams once stated that, "even if machines did contribute to normal science, we see no mechanism by which they could create a Kuhnian revolution and thereby establish a new physical law." In this Perspective
Externí odkaz:
http://arxiv.org/abs/2406.17836
Autor:
Zhang, Qianru, Wang, Haixin, Long, Cheng, Su, Liangcai, He, Xingwei, Chang, Jianlong, Wu, Tailin, Yin, Hongzhi, Yiu, Siu-Ming, Tian, Qi, Jensen, Christian S.
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques
Externí odkaz:
http://arxiv.org/abs/2405.09592
Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers. However, a significant c
Externí odkaz:
http://arxiv.org/abs/2402.08383
Autor:
Wu, Tailin, Maruyama, Takashi, Wei, Long, Zhang, Tao, Du, Yilun, Iaccarino, Gianluca, Leskovec, Jure
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical engineering to aerospace engineering. Inverse design is typically form
Externí odkaz:
http://arxiv.org/abs/2401.13171
Autor:
Han, Zhongyi, Zhou, Guanglin, He, Rundong, Wang, Jindong, Wu, Tailin, Yin, Yilong, Khan, Salman, Yao, Lina, Liu, Tongliang, Zhang, Kun
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foun
Externí odkaz:
http://arxiv.org/abs/2312.07424
Autor:
Zhang, Xuan, Wang, Limei, Helwig, Jacob, Luo, Youzhi, Fu, Cong, Xie, Yaochen, Liu, Meng, Lin, Yuchao, Xu, Zhao, Yan, Keqiang, Adams, Keir, Weiler, Maurice, Li, Xiner, Fu, Tianfan, Wang, Yucheng, Yu, Haiyang, Xie, YuQing, Fu, Xiang, Strasser, Alex, Xu, Shenglong, Liu, Yi, Du, Yuanqi, Saxton, Alexandra, Ling, Hongyi, Lawrence, Hannah, Stärk, Hannes, Gui, Shurui, Edwards, Carl, Gao, Nicholas, Ladera, Adriana, Wu, Tailin, Hofgard, Elyssa F., Tehrani, Aria Mansouri, Wang, Rui, Daigavane, Ameya, Bohde, Montgomery, Kurtin, Jerry, Huang, Qian, Phung, Tuong, Xu, Minkai, Joshi, Chaitanya K., Mathis, Simon V., Azizzadenesheli, Kamyar, Fang, Ada, Aspuru-Guzik, Alán, Bekkers, Erik, Bronstein, Michael, Zitnik, Marinka, Anandkumar, Anima, Ermon, Stefano, Liò, Pietro, Yu, Rose, Günnemann, Stephan, Leskovec, Jure, Ji, Heng, Sun, Jimeng, Barzilay, Regina, Jaakkola, Tommi, Coley, Connor W., Qian, Xiaoning, Qian, Xiaofeng, Smidt, Tess, Ji, Shuiwang
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range
Externí odkaz:
http://arxiv.org/abs/2307.08423
Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requir
Externí odkaz:
http://arxiv.org/abs/2305.01122