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pro vyhledávání: '"68u07"'
This paper presents an advanced method for addressing the inverse kinematics and optimal path planning challenges in robot manipulators. The inverse kinematics problem involves determining the joint angles for a given position and orientation of the
Externí odkaz:
http://arxiv.org/abs/2412.18294
Creating realistic VR experiences is challenging due to the labor-intensive process of accurately replicating real-world details into virtual scenes, highlighting the need for automated methods that maintain spatial accuracy and provide design flexib
Externí odkaz:
http://arxiv.org/abs/2412.11033
Autor:
Li, Ruifeng, Liu, Wei, Zhou, Xiangxin, Li, Mingqian, Zhang, Qiang, Chen, Hongyang, Lin, Xuemin
In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular property predict
Externí odkaz:
http://arxiv.org/abs/2410.20711
Autor:
Müller, Andreas
Publikováno v:
Computer Aided Geometric Design, Volume 113, 2024, 102366, ISSN 0167-8396
Whilst Paul de Casteljau is now famous for his fundamental algorithm of curve and surface approximation, little is known about his other findings. This article offers an insight into his results in geometry, algebra and number theory. Related to geom
Externí odkaz:
http://arxiv.org/abs/2408.13125
We construct over a given bilinear multi-patch domain a novel $C^s$-smooth mixed degree and regularity isogeometric spline space, which possesses the degree $p=2s+1$ and regularity $r=s$ in a small neighborhood around the edges and vertices, and the
Externí odkaz:
http://arxiv.org/abs/2407.17046
Optimal mean shift vector (OMSV)-based importance sampling methods have long been prevalent in yield estimation and optimization as an industry standard. However, most OMSV-based methods are designed heuristically without a rigorous understanding of
Externí odkaz:
http://arxiv.org/abs/2407.00711
Autor:
Lee, Jae Yong, Kim, Yeoneung
The framework of deep operator network (DeepONet) has been widely exploited thanks to its capability of solving high dimensional partial differential equations. In this paper, we incorporate DeepONet with a recently developed policy iteration scheme
Externí odkaz:
http://arxiv.org/abs/2406.10920
Differential equations are pivotal in modeling and understanding the dynamics of various systems, offering insights into their future states through parameter estimation fitted to time series data. In fields such as economy, politics, and biology, th
Externí odkaz:
http://arxiv.org/abs/2404.14873
Scientific computing using deep learning has seen significant advancements in recent years. There has been growing interest in models that learn the operator from the parameters of a partial differential equation (PDE) to the corresponding solutions.
Externí odkaz:
http://arxiv.org/abs/2402.08187
Fast and accurate predictions for complex physical dynamics are a significant challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in real-world problems. The deep operator network (DeepONe
Externí odkaz:
http://arxiv.org/abs/2312.15949