We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture to inform the network of the physics of the process, and to provide error correction and stabilization mechanisms. In addition, to compensate for decrease of observer's performance due to the presence of unknown destabilizing forcing, the network is designed to estimate the contribution of the unknown forcing implicitly from the data over the course of training. By running a set of numerical experiments, we demonstrate that the proposed network does recover unknown forcing from data and is capable of predicting turbulent flows in high resolution from low resolution noisy observations.
Physics-informed post-processing of stabilized finite element solutions for transient convection-dominated problems
Department of Computer Technologies, Antalya Bilim University（安塔利亚比林大学计算机技术系）；Institute of Applied Mathematics, Middle East Technical University（中东技术大学应用数学研究所）；Department of Mathematics, Indian Institute of Technology Guwahati（印度理工学院古瓦哈提数学系）
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
Institution for Foundation of Data Science, Yale University（数据科学基础研究所，耶鲁大学）；Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（机械工程与应用力学系，宾夕法尼亚大学）；Sibley School of Mechanical and Aerospace Engineering, Cornell University（机械与航空航天工程学院，康奈尔大学）；Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory（先进计算、数学和数据部门，太平洋西北国家实验室）
Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA（应用数学系，布朗大学）；School of Engineering, Brown University, Providence, RI 02912, USA（工程学院，布朗大学）
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Institution for Foundation of Data Science, Yale University（数据科学基础研究所，耶鲁大学）；Penn Institute for Computational Science, University of Pennsylvania（宾夕法尼亚大学计算科学研究所）；Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（宾夕法尼亚大学机械工程与应用力学系）
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA（机械与工业工程系，东北大学，波士顿，马萨诸塞州，02115，美国）；School of Mechanical Engineering, University of Leeds, Leeds, UK（机械工程学院，利兹大学，利兹，英国）
SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment
Western University（西安大略大学）；MIT Plasma Science and Fusion Center（麻省理工学院等离子体科学与聚变中心）
边缘等离子体湍流对磁约束聚变装置的性能至关重要。为了更好地理解理论和实验中的边缘湍流，开发了一个受偏微分方程约束的定制物理学深度学习框架，以准确地从电子压力的部分观测中学习与双流体理论一致的湍流场。这种计算是不可能的，否则使用传统的平衡模型。有了这种技术，第一个直接的定量比较静电双流体理论和电磁gyrokinetic模型之间的湍流场表现出良好的整体协议，发现在磁化螺旋等离子体在低归一化压力。为了将这些计算技术转化为实验聚变等离子体，通过一个新创建的深度学习框架展示了一种将HeI线辐射的亮度测量转化为局部等离子体波动的新方法，该框架集成了中性输运物理和碰撞辐射理论，用于氦原子中的$3^3 D - 2^3 P$跃迁。本论文利用Alcator C-Mod托卡马克装置上的快速相机数据，首次利用单根谱线对聚变等离子体中湍流电子密度、电子温度和中性密度进行了二维时间相关的实验测量。利用实验推断的数据，计算了轴对称聚变等离子体纯环向场框架下符合漂移简化Braginskii理论的二维湍流电场的初始估计值.发现氦原子对粒子和能量源的影响增强了电场和电子压力之间的相关性，同时加宽了影响${\bf E \times B}$流动和剪切速率的湍流场幅度。
A term-by-term variational multiscale method with dynamic subscales for incompressible turbulent aerodynamics
Computational Heat and Fluid Flow Lab, Universidad de Santiago de Chile（圣地亚哥智利大学计算热与流体流动实验室）；Department of Mechanical Engineering, University of Santiago de Chile（圣地亚哥智利大学机械工程系）；Chair for Computational Analysis of Technical Systems, RWTH Aachen University（亚琛工业大学技术系统计算分析教席）；Chair of Methods for Model-based Development in Computational Engineering, RWTH（亚琛工业大学计算工程基于模型开发方法教席）；Center for Simulation and Data Science (JARA-CSD), RWTH Aachen University（亚琛工业大学仿真与数据科学中心（JARA-CSD））
Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
College of Renewable Energy, Hohai University, Changzhou, 213200, China（能源学院，河海大学，常州，213200，中国）；College of Water Conservancy and Hydropower Engineering（水利水电工程学院）；National Technology Innovation Center for Wind Power, Hohai University, Changzhou, 213200, China（风能技术创新中心，河海大学，常州，213200，中国）
Data assimilation in 2D incompressible Navier-Stokes equations, using a stabilized explicit $O(Δt)^2$ leapfrog finite difference scheme run backward in time
