Simulation Study
Simulation studies encompass the use of computational models to investigate complex systems and processes across diverse scientific domains. Current research emphasizes developing sophisticated models, including deep neural networks, agent-based models, and generative models, to enhance realism, efficiency, and the ability to handle large-scale datasets. These studies are crucial for testing hypotheses, optimizing designs, and predicting outcomes in scenarios ranging from weather forecasting and traffic flow to robotic control and drug discovery, ultimately advancing scientific understanding and informing practical applications. The increasing integration of large language models further expands the scope and accessibility of simulation studies.
Papers
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
Junting Chen, Yao Mu, Qiaojun Yu, Tianming Wei, Silang Wu, Zhecheng Yuan, Zhixuan Liang, Chao Yang, Kaipeng Zhang, Wenqi Shao, Yu Qiao, Huazhe Xu, Mingyu Ding, Ping Luo
Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study
Johann Haselberger, Maximilian Böhle, Bernhard Schick, Steffen Müller
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
Inna Wanyin Lin, Ashish Sharma, Christopher Michael Rytting, Adam S. Miner, Jina Suh, Tim Althoff
Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
Daniel Jakab, Brian Michael Deegan, Sushil Sharma, Eoin Martino Grua, Jonathan Horgan, Enda Ward, Pepijn Van De Ven, Anthony Scanlan, Ciarán Eising