In recent years, there have been a surge in applications of neural networks (NNs) in physical sciences. Although various algorithmic advances have been proposed, there are, thus far, limited number of studies that assess the interpretability of neural networks. This has contributed to the hasty characterization of most NN methods as "black boxes" and hindering wider acceptance of more powerful machine learning algorithms for physics. In an effort to address such issues in fluid flow modeling, we use a probabilistic neural network (PNN) that provide confidence intervals for its predictions in a computationally effective manner. The model is first assessed considering the estimation of proper orthogonal decomposition (POD) coefficients from local sensor measurements of solution of the shallow water equation. We find that the present model outperforms a well-known linear method with regard to estimation. This model is then applied to the estimation of the temporal evolution of POD coefficients with considering the wake of a NACA0012 airfoil with a Gurney flap and the NOAA sea surface temperature. The present model can accurately estimate the POD coefficients over time in addition to providing confidence intervals thereby quantifying the uncertainty in the output given a particular training data set.
FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
ETSIAE-UPM-School of Aeronautics, Universidad Politécnica de Madrid（马德里理工大学航空学院）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC, CSIC-UIB)（跨学科物理与复杂系统研究所（IFISC，CSIC-UIB））；Center for Computational Simulation, Universidad Politécnica de Madrid（马德里理工大学计算模拟中心）
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Applied Modelling and Computation Group, Department of Earth Science and Engineering（应用建模与计算组，地球科学与工程系）；Institute of GeoEnergy Engineering（地球能源工程研究所）；Department of Civil and Environmental Engineering（土木与环境工程系）；Centre for AI-Physics Modelling, Imperial-X, White City Campus（人工智能-物理建模中心，Imperial-X，白城校区）；Data Assimilation Lab., Data Science Institute（数据同化实验室，数据科学研究院）
Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields
Interdisciplinary Graduate Programme（交叉学科研究生项目）；Nanyang Technological University（南洋理工大学）；School of Future Technology（未来技术学院）；Fuzhou University（福州大学）；College of Computing and Data Science（计算与数据科学学院）；Yunnan University（云南大学）；School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
Division of Advanced Nuclear Engineering, Pohang University of Science and Technology（浦项国立科学技术大学先进核工程系）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）；Graduate School of Integrated Energy-AI, Jeonbuk National University（全北国立大学整合能源-人工智能研究生院）
Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
Simulation (CHAOS) Laboratory, Department of Chemical Engineering, Polytechnique Montr\'eal, 2500 chemin de Polytechnique, QC, Canada, H3T 1J4；National Research Council Canada, Boucherville, QC, Canada, J4B 6Y4；Department of Chemical Engineering, Polytechnique Montr\'eal, PO Box 6079, Stn Centre-Ville, Montr\'eal, QC, Canada, H3C 3A7.
Inpainting physics: self-supervised learning for context-driven fluid simulation
AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（图像引导诊断与治疗人工智能，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；AI in Healthcare and Medicine, Technical University of Munich（医疗人工智能，慕尼黑技术大学）；Imperial College London（伦敦帝国学院）
Explainable quantum-compressed machine learning for complex fluid flows
Centre for Computational Science, University College London（计算科学中心，伦敦大学学院）；Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities（巴伐利亚科学院和人文科学莱比锡超算中心）
A Mesh-Adaptive Hypergraph Neural Network for Unsteady Flow Around Oscillating and Rotating Structures
Department of Mechanical Engineering, The University of British Columbia（不列颠哥伦比亚大学机械工程系）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）
Statistical and probabilistic modeling of a cloud of particles coupled with a turbulent fluid
本文提出了一种新的探索性的形式主义，其最终目标是数值模拟的动力学粒子云弱或强耦合的湍流流体。鉴于作者名单的专业知识，本文的内容扫描了广泛的连接概念，从湍流的物理学到随机过程的严格定义。我们的方法是开发降阶模型的动力学携带和携带阶段，这在此形式主义中是一致的，并建立一个数值过程来验证这些模型。本文的新颖之处在于收集了大量的数学和物理定义和结果，并在一个共同的框架和一个商定的词汇（第1和2节），并在此范围内的一些初步结果和成就，第3节。虽然前三个部分已被简化为一个气田的背景下，分散相只retrieves energy through drag，第四部分打开这项研究的更复杂的情况下，dispersephase相互作用的连续相，以及在一个能量守恒的方式。这将使我们能够揭示该项目的观点并得出结论。
