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
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation
Nathan Phelps, Stephanie Marrocco, Stephanie Cornell, Dalton L. Wolfe, Daniel J. Lizotte
Robotic Arm Manipulation to Perform Rock Skipping in Simulation
Nicholas Ramirez, Michael Burgess
Curriculum-based Sensing Reduction in Simulation to Real-World Transfer for In-hand Manipulation
Lingfeng Tao, Jiucai Zhang, Qiaojie Zheng, Xiaoli Zhang
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
Md Nasim, Anter El-Azab, Xinghang Zhang, Yexiang Xue
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling