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
Simulation of machine learning-based 6G systems in virtual worlds
Ailton Oliveira, Felipe Bastos, Isabela Trindade, Walter Frazao, Arthur Nascimento, Diego Gomes, Francisco Muller, Aldebaro Klautau
A review of path following control strategies for autonomous robotic vehicles: theory, simulations, and experiments
Nguyen Hung, Francisco Rego, Joao Quintas, Joao Cruz, Marcelo Jacinto, David Souto, Andre Potes, Luis Sebastiao, Antonio Pascoal
Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature
Guodao Zhang, Shahab S. Band, Sina Ardabili, Kwok-Wing Chau, Amir Mosavi
Bond Graph Modelling and Simulation of Pneumatic Soft Actuator
Garima Bhandari, Pushparaj Mani Pathak, Jung-Min Yang