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pro vyhledávání: '"Li, Shibo"'
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods. These methods typically require extensive data for training due to their large
Externí odkaz:
http://arxiv.org/abs/2404.14815
Multi-fidelity surrogate learning is important for physical simulation related applications in that it avoids running numerical solvers from scratch, which is known to be costly, and it uses multi-fidelity examples for training and greatly reduces th
Externí odkaz:
http://arxiv.org/abs/2311.05606
Publikováno v:
The Twelfth International Conference on Learning Representations (ICLR 2024)
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs). However, PINNs often struggle to solve high-frequency and multi-scale PDE
Externí odkaz:
http://arxiv.org/abs/2311.04465
Publikováno v:
The Twelfth International Conference on Learning Representations (ICLR 2024)
Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a core tensor and a set of object representations (factors). A fundamen
Externí odkaz:
http://arxiv.org/abs/2311.04829
Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however, often under-use the time information and ignore the structural knowl
Externí odkaz:
http://arxiv.org/abs/2310.19666
Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and hence cannot capture the temporal evolution of the objects' represent
Externí odkaz:
http://arxiv.org/abs/2310.17021
Fourier Neural Operator (FNO) is a popular operator learning framework. It not only achieves the state-of-the-art performance in many tasks, but also is efficient in training and prediction. However, collecting training data for the FNO can be a cost
Externí odkaz:
http://arxiv.org/abs/2309.16971
Publikováno v:
Xibei zhiwu xuebao, Vol 44, Iss 11, Pp 1828-1830 (2024)
[Objective] Gastrodia elata Bl. f. cyaneum L. B. Lin represents a newly identified form of Gastrodia originated from Xiaocaoba Town of Zhaotong in Yunnan, providing comprehensive documentation of morphological characteristics with color photographs
Externí odkaz:
https://doaj.org/article/8a3174349960473fb6b725d945426fbd
Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for these applications is often costly, e.g. by running numerical solvers. Th
Externí odkaz:
http://arxiv.org/abs/2210.12704
Autor:
Li, Shibo, Penwarden, Michael, Xu, Yiming, Tillinghast, Conor, Narayan, Akil, Kirby, Robert M., Zhe, Shandian
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply different PINNs to solve the problem in each subdomain, and stitch the subdoma
Externí odkaz:
http://arxiv.org/abs/2210.12669