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
Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities
Philippe J. Giabbanelli, Jose J. Padilla, Ameeta Agrawal
A randomized simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management
Abdelmalek Mouazer, Sophie Dubois, Romain Léguillon, Nada Boudegzdame, Thibaud Levrard, Yoann Le Bars, Christian Simon, Brigitte Séroussi, Julien Grosjean, Romain Lelong, Catherine Letord, Stéfan Darmoni, Karima Sedki, Pierre Meneton, Rosy Tsopra, Hector Falcoff, Jean-Baptiste Lamy
Improving Generalization of Speech Separation in Real-World Scenarios: Strategies in Simulation, Optimization, and Evaluation
Ke Chen, Jiaqi Su, Taylor Berg-Kirkpatrick, Shlomo Dubnov, Zeyu Jin
Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation
Saul Schaffer, Hima Hrithik Pamu, Victoria A. Webster-Wood
Towards Fully Autonomous Research Powered by LLMs: Case Study on Simulations
Zhihan Liu, Yubo Chai, Jianfeng Li
AeroVerse: UAV-Agent Benchmark Suite for Simulating, Pre-training, Finetuning, and Evaluating Aerospace Embodied World Models
Fanglong Yao, Yuanchang Yue, Youzhi Liu, Xian Sun, Kun Fu