Narrative Review
Narrative reviews synthesize existing research to provide a comprehensive overview of a specific topic, aiming to identify key trends, gaps, and future research directions. Current research focuses on applying narrative reviews across diverse fields, employing various model architectures like graph neural networks, large language models, and diffusion models to analyze complex data and improve model interpretability and efficiency. This approach is crucial for advancing scientific understanding and informing the development of practical applications in areas such as medicine, engineering, and manufacturing.
Papers
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N. Vlassis, Moritz Flaschel, Pietro Carrara, Laura De Lorenzis
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review
Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr, Kristian Muri Knausgård
Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
Chenlin Zhou, Han Zhang, Liutao Yu, Yumin Ye, Zhaokun Zhou, Liwei Huang, Zhengyu Ma, Xiaopeng Fan, Huihui Zhou, Yonghong Tian
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
Jamil Zaghir, Marco Naguib, Mina Bjelogrlic, Aurélie Névéol, Xavier Tannier, Christian Lovis
Wildfire Risk Prediction: A Review
Zhengsen Xu, Jonathan Li, Sibo Cheng, Xue Rui, Yu Zhao, Hongjie He, Linlin Xu