Spacecraft Collision Risk Assessment with Probabilistic Programming
University of Strathclyde（思克莱德大学）；University of Oxford（牛津大学）；Technische Universität Darmstadt（达姆施塔特工业大学）；IBM（国际商业机器公司（IBM））；Imperial College London（伦敦帝国学院）；European Space Agency（欧洲空间局）；University of Surrey（萨里大学）
Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges… 展开作者
Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin
备注：
8 pages, 2 figures
发表信息：
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada
Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. Furthermore, the predicted growth of the space sector and the planned launch of megaconstellations will add even more complexity, therefore causing the collision risk and the burden on space operators to increase. Managing this complex framework with internationally agreed methods is pivotal and urgent. In this context, we build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages, calibrated using real data. By conditioning on observations, we use the model to obtain posterior distributions via Bayesian inference. We show that the probabilistic programming approach to conjunction assessment can help in making predictions and in finding the parameters that explain the observed data in conjunction data messages, thus shedding more light on key variables and orbital characteristics that more likely lead to conjunction events. Moreover, our technique enables the generation of physically accurate synthetic datasets of collisions, answering a fundamental need of the space and machine learning communities working in this area.
Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps
Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA（加州大学伯克利分校土木与环境工程系）；D3 Analytics Innovation, Allstate Insurance Company, Northbrook, IL, USA（Allstate保险公司D3 Analytics创新部门）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA（加州大学伯克利分校电气工程与计算机科学系）
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology（工业和信息化部智能防灾减灾重点实验室）；School of Civil Engineering, Harbin Institute of Technology（哈尔滨工业大学土木工程学院）；Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education（教育部结构动力行为与控制重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
A Knowledge-Informed Large Language Model Framework for U.S. Nuclear Power Plant Shutdown Initiating Event Classification for Probabilistic Risk Assessment
University of Idaho（爱达荷大学）；Idaho National Laboratory（爱达荷国家实验室）
GraphVSSM: Graph Variational State-Space Model for Probabilistic Spatiotemporal Inference of Dynamic Exposure and Vulnerability for Regional Disaster Resilience Assessment
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Mila – Québec Artificial Intelligence Institute（魁北克人工智能研究所）；Ciela Institute（Ciela研究所）；Université de Montréal（蒙特利尔大学）；SanboxAQ；CCA – Flatiron Institute（CCA-Flatiron研究所）；University of Edinburgh（爱丁堡大学）；Trottier Space Institute（特罗特里尔空间研究所）
