Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics and uncertainties, making traditional numerical approaches infeasible. To address these concerns, researchers have proposed data-driven approaches to solve the PF problem by learning the mapping rules from historical system operation data. Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems. In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws and system topology into the rebuilt PF model, our neural-network based PF solver is regularized by the auxiliary task and constrained by the physical laws. The simulation results show that our physics-guided neural network methods achieve better performance and generalizability compared to existing unconstrained data-driven approaches. Furthermore, we demonstrate that the weight matrices of our physics-guided neural networks embody power system physics by showing their similarities with the bus admittance matrices.
College of Engineering, Peking University（北京大学工学院）；School of Environmental Science and Engineering, Southern University of Science and Technology（南方科技大学环境科学与工程学院）；Peng Cheng Laboratory（鹏城实验室）
Subsurface flow problems usually involve some degree of uncertainty. Consequently, uncertainty quantification is commonly necessary for subsurface flow prediction. In this work, we propose a methodology for efficient uncertainty quantification for dynamic subsurface flow with a surrogate constructed by the Theory-guided Neural Network (TgNN). The TgNN here is specially designed for problems with stochastic parameters. In the TgNN, stochastic parameters, time and location comprise the input of the neural network, while the quantity of interest is the output. The neural network is trained with a…
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；Technical University of Munich（慕尼黑技术大学）；Foundation Robotics Labs, Munich（慕尼黑基础机器人实验室）；Eötvös Loránd University（劳尔·艾尔多迪大学）
We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers. We use a deep feed-forward neural network trained with load-flows precomputed by simulation. Our architecture permits to train a network on so-called "n-1" problems, in which load flows are evaluated for every possible line disconnection, then generalize to "n-2" problems without retraining (a clear advantage because of the combinatorial nature of the problem). To that e…
Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization
i2Cat Foundation（i2Cat基金会）；NEC Laboratories Europe GmbH（欧洲NEC实验室）；ICREA（加泰罗尼亚研究与高级研究所）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；TU Berlin（柏林工业大学）；University of Antwerp（安特卫普大学）
3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow …
Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimization approach to minimize an object's drag coefficient in laminar flow based on predicting drag directly from the object shape. Jointly training an architecture combining a variational autoencoder mapping shapes to latent representations and Gaussian process regression allows us to generate improved shapes in the two dimensional case we consider.
The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion -> pixel propagation -> synthesis. However, there is a significant drawback that the errors in each step continue to accumulate and amplify in the next step. To this end, we propose an Error Compensation Framework for Flow-guided Video Inpainting (ECFVI), which takes advantage of the flow-based method and offsets its weaknesses. We address the weakness with the newly designed flow …
Idaho National Laboratory（爱达荷国家实验室）；Massachusetts Institute of Technology（麻省理工学院）；North Carolina State University（北卡罗来纳州立大学）
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-…
College of Engineering, Peking University（北京大学工学院）；School of Environmental Science and Engineering, Southern University of Science and Technology（南方科技大学环境科学与工程学院）
Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter's effectiveness. In this study, the Theory-guided Neural Network (TgNN) is proposed for deep learning of subsurface flow. In the TgNN, as supervised learning, the neural network is trained with available observations or simulation data while being simultaneously guided by theory (e.g., governing equations, other physical constraints, engineering controls, and expert knowledge) of the underlying problem. The TgNN can…
Global Transport Couplings for Classifier-Free Guided Flows
AITHYRA；University of Oxford（牛津大学）；Mila - Québec AI Institute（Mila - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Massachusetts Institute of Technology（麻省理工学院）；Technische Universität Wien（维也纳工业大学）；Flatiron Institute（熨斗研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
Mechanical and Industrial Engineering, University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校机械与工业工程系）；Department of Mechanical Engineering, Hakim Sabzevari University（哈基姆·萨布兹瓦里大学机械工程系）
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）；School of Artificial Intelligence, Beihang University（北航人工智能学院）；School of Mathematics, Shandong University（山东大学数学学院）
Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data
i2Cat Foundation（i2Cat基金会）；NEC Laboratories Europe（欧洲NEC实验室）；ICREA（加泰罗尼亚研究与高级研究所）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；University of Antwerp（安特卫普大学）
Minimizing Structural Vibrations via Guided Flow Matching Design Optimization
Institute of Informatics, University of Göttingen（信息学院，哥廷根大学）；Institute for Acoustics and Dynamics, Technische Universität Braunschweig（声学与动力学研究所，布拉unsch维格技术大学）；Campus Institute Data Science (CIDAS), University of Göttingen（哥廷根大学校园数据科学研究所（CIDAS））
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is unable to capture all of the scales of turbulent transport accurately. Our goal in this work is to build a new data-driven methodology based on super-resolution techniques to reconstruct DNS data from LES predictions. We leverage the underlying physical relationships to regularize the relationships amongst different physical variables. We also introduce a hierarc…
SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University（同济大学土木工程学院地质工程系）；Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University（教育部同济大学地质与地下工程重点实验室）；Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST)（卡布斯科学与技术大学（KAUST）物理科学与工程系）；State Key Laboratory of Marine Geology, Tongji University（同济大学海洋地质国家重点实验室）；Tencent SSV（腾讯SSV）；Tencent SD（腾讯SD）
Path-Guided Flow Matching for Dataset Distillation
Department of Machine Learning, MBZUAI, Abu Dhabi, UAE（机器学习系，MBZUAI，阿布扎赫德，阿联酋）；Department of Computer Vision, MBZUAI, Abu Dhabi, UAE（计算机视觉系，MBZUAI，阿布扎赫德，阿联酋）
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系）；Department of Computer Science, Fudan University（复旦大学计算机科学系）；Center of Computational Biology, Duke-NUS Medical School（杜克-新加坡国立大学医学学校计算生物学中心）；Management and Technology Program, University of Pennsylvania（宾夕法尼亚大学管理与技术项目）；Department of Computer Science, Duke University（杜克大学计算机科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）
提出 MOG-DFM 框架，通过混合秩方向分数和自适应超锥滤波引导预训练离散流匹配模型，实现多目标帕累托高效的肽与增强子 DNA 序列设计。
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Microsoft Research AI4Science（微软研究院AI4科学部门）；Xi’an Jiaotong University（西安交通大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；HEC Montréal（蒙特利尔高等商学院）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
Sichuan University（四川大学）；Chongqing Three Gorges University（重庆三峡学院）；Megvii Inc（旷视科技有限公司）；Skill Training Center of Sichuan Electric Power Corporation of State Grid（国家电网四川省电力公司技能培训中心）；State Grid Sichuan Electric Power Research Institute（国网四川省电力公司电力科学研究院）
University of Pittsburgh（匹兹堡大学）；U.S. Geological Survey（美国地质调查局）；University of Minnesota（明尼苏达大学）
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we present a pre-training technique which transfers knowledge from physics-based models to initialize the machine learning model and learn the physics of streamflow and thermodynamics. We also propose a new loss function that balances the performance over differen…
Generalizable single-cell perturbation response prediction using energy-guided flow matching
Zhejiang University（浙江大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies（北京大学成都前沿交叉生物技术研究院）
Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）
The theory-guided convolutional neural network (TgCNN) framework, which can incorporate discretized governing equation residuals into the training of convolutional neural networks (CNNs), is extended to two-phase porous media flow problems in this work. The two principal variables of the considered problem, pressure and saturation, are approximated simultaneously with two CNNs, respectively. Pressure and saturation are coupled with each other in the governing equations, and thus the two networks are also mutually conditioned in the training process by the discretized governing equations, which…
