This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems. We assume access to a computationally complex simulator that inputs a candidate parameter and outputs a corresponding multichannel time series. Our task is to accurately estimate a range of likely values of the underlying parameters. Standard iterative approaches necessitate running the simulator many times, which is computationally prohibitive. This paper describes a novel framework for learning feature embeddings of observed dynamics jointly with an emulator that can replace high-cost simulators for parameter estimation. Leveraging a contrastive learning approach, our method exploits intrinsic data properties within and across parameter and trajectory domains. On a coupled 396-dimensional multiscale Lorenz 96 system, our method significantly outperforms a typical parameter estimation method based on predefined metrics and a classical numerical simulator, and with only 1.19% of the baseline's computation time. Ablation studies highlight the potential of explicitly designing learned emulators for parameter estimation by leveraging contrastive learning.
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
Servicio Meteorológico Nacional（国家气象局）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（国家科学技术研究委员会）；Centro de Investigaciones del Mar y la Atmósfera（海洋与大气研究中心）；Instituto Franco-Argentino para el Estudio del Clima y sus Impactos（法阿气候及其影响研究所）；Universidad Nacional del Nordeste（东北国立大学）；IMT Atlantique（大西洋国立高等矿业电信学校）；CNRS（法国国家科学研究中心）；IRD（法国可持续发展研究所）
Deep set based operator learning with uncertainty quantification
Department of Mathematics, Shanghai Normal University（上海师范大学数学系）；School of Mathematical Sciences, Institute of Natural Sciences（自然科学院数学系）；MOE-LSC, Shanghai Jiao Tong University（教育部重点实验室，上海交通大学）；Institute of Computational Mathematics（计算数学研究所）；Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences（科学与工程计算，中国科学院数学研究所）
Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching
Department of Information Technology, Uppsala University, Uppsala, Sweden（瑞典乌普萨拉大学信息科技系）；Science for Life Laboratory, Uppsala University, Uppsala, Sweden（瑞典乌普萨拉大学生命科学实验室）
Eigenvalue Calibration for Semantic Embeddings of Large Language Models
KU Leuven（鲁汶大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；Goethe University Frankfurt（法兰克福歌德大学）；PSL Research University / Inria（巴黎文理研究大学/法国国家信息与自动化研究所）；Frankfurt Cancer Institute（法兰克福癌症研究所）
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）；Pacific Northwest National Laboratory, Richland, WA, USA（太平洋西北国家实验室）；Department of Statistics, North Carolina State University, Raleigh, NC, USA（统计系，北卡罗来纳州立大学）
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；Technology Innovation Institute（技术创新研究所）；HSE University（高等经济大学）；Nuro, Inc.（Nuro公司）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；AIRI；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Department of Biostatistics（生物统计学系）；Harvard University（哈佛大学）；Department of Epidemiology and Biostatistics（流行病学与生物统计学系）；University of South Carolina（南卡罗来纳大学）；LTCI Telecom Paris Institut Polytechnique de Paris（巴黎电信理工学院LTCI）；UCLA（加州大学洛杉矶分校）
Generative imaging for radio interferometry with fast uncertainty quantification
Mullard Space Science Laboratory (MSSL)（穆拉德空间科学实验室）；University College London (UCL)（伦敦大学学院）；Leiden Observatory（莱顿天文台）；Leiden University（莱顿大学）；IRFU CEA Université Paris-Saclay（CEA巴黎-萨克雷大学IRFU）；Department of Computer Science（计算机科学系）；Alan Turing Institute（艾伦·图灵研究所）
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Peking University（北京大学）；Massachusetts Institute of Technology（麻省理工学院）；University College London（伦敦大学学院）；University of Chinese Academy of Sciences（中国科学院大学）；Chengdu University of Technology（成都理工大学）
Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
Center for Integrated Nanotechnologies（集成纳米技术中心）；Sandia National Laboratories（桑塔纳国家实验室）；Theoretical Division（理论 division）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Department of Mechanical Engineering（机械工程系）；Pohang University of Science and Technology（釜山科学技术大学）
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Cornell University（康奈尔大学）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Sandia National Laboratories（桑迪亚国家实验室）；University of Southern California（南加州大学）；Pasteur Labs（巴斯德实验室）
Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows
Center for Interdisciplinary Exploration and Research in Astrophysics（跨学科天体物理探索与研究中心）；Northwestern University（西北大学）；The NSF-Simons AI Institute for the Sky (NSF-Simons SkAI)（NSF-模拟斯诺人工智能天空研究所）
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
