plasma
发表于 2025-3-28 15:45:05
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Germinate
发表于 2025-3-28 22:25:42
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Hdl348
发表于 2025-3-28 23:16:04
Conference proceedings 2019pical sections on simulation and modeling methodology; numerical and Monte Carlo simulation; simulation applications: blockchain, deep learning and cloud; simulation and visualization; simulation applications; short papers. .
PRISE
发表于 2025-3-29 03:51:59
1865-0929 ized in topical sections on simulation and modeling methodology; numerical and Monte Carlo simulation; simulation applications: blockchain, deep learning and cloud; simulation and visualization; simulation applications; short papers. .978-981-15-1077-9978-981-15-1078-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
Working-Memory
发表于 2025-3-29 10:11:46
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ABASH
发表于 2025-3-29 13:55:19
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Platelet
发表于 2025-3-29 18:40:23
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EWE
发表于 2025-3-29 21:06:09
Robot Arm Control Method of Moving Below Object Based on Deep Reinforcement Learningrly designed. As is shown by the results, the control agent trained in this paper show good performance in controlling the robot arm, which in turn confirms the effectiveness of the training algorithm with effective data support of the constructed simulation environment.
CORD
发表于 2025-3-30 00:48:43
On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulationslatforms, exploiting Rust as programming language. The Rust-AB architecture as well as an investigation on the ability of Rust to develop ABM simulations are discussed. An ABM simulation written in Rust-AB, and a performance comparison against the well-adopted Java ABM toolkit MASON is also presented.
WATER
发表于 2025-3-30 07:01:06
Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenariosto predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.