Physically Consistent Neural Networks for building thermal modeling: theory and analysis
Swiss Federal Laboratories for Materials Science and Technology (Empa)（瑞士联邦材料科学与技术实验室（Empa））；Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin N. Jones
备注：
Preprint submitted to Applied Energy. 13 pages in the main text + 5 in appendix, 11 figures
Due to their high energy intensity, buildings play a major role in the current worldwide energy transition. Building models are ubiquitous since they are needed at each stage of the life of buildings, i.e. for design, retrofitting, and control operations. Classical white-box models, based on physical equations, are bound to follow the laws of physics but the specific design of their underlying structure might hinder their expressiveness and hence their accuracy. On the other hand, black-box models are better suited to capture nonlinear building dynamics and thus can often achieve better accuracy, but they require a lot of data and might not follow the laws of physics, a problem that is particularly common for neural network (NN) models. To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues.
In this work, we present a novel physics-informed NN architecture, dubbed Physically Consistent NN (PCNN), which only requires past operational data and no engineering overhead, including prior knowledge in a linear module running in parallel to a classical NN. We formally prove that such networks are physically consistent - by design and even on unseen data - with respect to different control inputs and temperatures outside and in neighboring zones. We demonstrate their performance on a case study, where the PCNN attains an accuracy up to 40% better than a classical physics-based resistance-capacitance model on 3-day long prediction horizons. Furthermore, despite their constrained structure, PCNNs attain similar performance to classical NNs on the validation data, overfitting the training data less and retaining high expressiveness to tackle the generalization issue.
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
PhD Candidate, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系博士研究生）；Group Supervisor, Dynamics, Modeling, Flight Mechanics & UQ, 4800 Oak Grove Dr, M/S 198-326, Pasadena, CA 91011, AIAA Member（加州帕萨迪纳AIAA会员，动力学、建模、飞行力学及不确定性量化组负责人）；Assistant Professor, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系助理教授）；Assistant Professor, Center for Statistics and Machine Learning, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学统计与机器学习中心助理教授）
Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
University of British Columbia（不列颠哥伦比亚大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；NVIDIA（英伟达）；Peking University（北京大学）；Foundry Biosciences（Foundry 生物科技）；University of Utah（犹他大学）
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
Sorbonne University（索邦大学）；Institut des Systèmes Intelligents et de Robotique（智能系统与机器人研究所）；Criteo AI Lab Paris（Criteo巴黎AI实验室）；Centre of Robotics（机器人中心）；MINES Paris-PSL PSL University（巴黎矿学院PSL大学）
ENS Paris-Saclay（巴黎-萨克雷高等师范学院）；Mines Paris（巴黎矿业学院）；Institut de Radioprotection et de Sûreté Nucléaire（法国辐射防护与核安全研究所）
Temperature dependence of the neutron-nucleus interaction is known as the Doppler broadening of the cross-sections. This is a well-known effect due to the thermal motion of the target nuclei that occurs in the neutron-nucleus interaction. The fast computation of such effects is crucial for any nuclear application. Mechanisms have been developed that allow determining the Doppler effects in the cross-section, most of them based on the numerical resolution of the equation known as Solbrig's kernel, which is a cross-section Doppler broadening formalism derived from a free gas atoms distribution h…
Gradient Networks for Universal Magnetic Modeling of Synchronous Machines
Department of Electrical Engineering and Automation, Aalto University（电气工程与自动化系，阿alto大学）；Aalto University House of AI（阿alto大学人工智能学院）；Research Council of Finland Centre of Excellence in High-Speed Electromechanical Energy Conversion Systems（芬兰研究理事会高速电磁能量转换系统卓越中心）；Aalto School of Electrical Engineering（阿alto电气工程学院）；ABB Oy, Drives（ABB Oy，驱动部门）
This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. These models are developed with the objective of reducing energy consumption costs while controlling the indoor temperature of the building within required comfort limits. To combine the interpretability of white/gray box physics models and the expressive power of neural networks, we propose a physics informed neural network approach for this modeling task. Along with measured data and building parameters, we encode the neural networks with the underlying physics that governs the th…
Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics knowledge cannot generalize well. This study proposes a deep supervised domain adaptation (DSDA) method for thermal dynamics modeling of building indoor temperature evolution and energy consumption. A long short term memory network based Sequence to Sequence scheme is pre-traine…
PhysFormer: A Physics-Embedded Generative Model for Physically Self-Consistent Spectral Synthesis
School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）；Weihai Institute for Interdisciplinary Research, Shandong University（威海联合研究 institute, 山东大学）
Technische Universität Dresden（德累斯顿工业大学）；Max Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden（德累斯顿系统生物学中心）；Flatiron Institute（熨斗研究所）；LMU München（慕尼黑大学）；Helmholtz Zentrum München（亥姆霍兹慕尼黑研究中心）；Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI（可扩展数据分析与人工智能中心（ScaDS.AI））；Cluster of Excellence Physics of Life, TU Dresden（德累斯顿工业大学卓越生命物理集群）；ONI Inc.（ONI公司）
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when learning or inferring differential-equation models from measurement data. Directly learning $\textit{interpretable}$ mathematical models from data has emerged as a valuable modeling approach. However, in areas like biology, high noise levels, sensor-induced correlations, and strong inter-system variability can render data-driven models nonsensical or physically inconsistent without additional constraints on …
Indian Institute of Technology, Gandhinagar（印度理工学院甘地讷格尔分校）
Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic parameter values in certain instances, ML algorithms establish the input-output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may…
Imperial College London（帝国理工学院）；Microsoft Research（微软研究院）；University College London（伦敦大学学院）
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. By leveraging recent work connecting deep neural networks to systems of differential equations, we propose \emph{variational integrator networks}, a class of neural network architectures designed to preserve the geometric structure of physical systems. This class of network architectures facilitates accurate long-term prediction, interpretability, and data-efficient learning, while still remaining highly flexible and capable of modeling complex behavior. We dem…
Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, they might consequently make physically inconsistent predictions, which makes them unreliable in practice. In this paper, we leverage the framework of Irreversible port-Hamiltonian Systems (IPHS), which can describe most multi-physics systems, and rely on Neural Ordinary Differential Equations (NODEs) to learn their parameters from data. Since IPHS models are consistent with the first and second principles of thermodyn…
Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
Earth and Environmental Science Area, Lawrence Berkeley National Lab（伯克利国家实验室地球与环境科学部）；Department of Hydrology and Atmospheric Science, University of Arizona（亚利桑那大学水文学与大气科学系）；School for the Environment, University of Massachusetts Boston（马萨诸塞大学波士顿分校环境学院）；Department of Civil Engineering, The University of Hong Kong（香港大学土木工程系）
Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地质科学学院经济地质学局）；State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences（中国科学院测量与地球物理研究所大地测量与地球动力学国家重点实验室）；Texas Advanced Computing Center, The University of Texas at Austin（德克萨斯大学奥斯汀分校德克萨斯高级计算中心）；Department of Geology and Geophysics, Indian Institute of Technology Kharagpur（印度理工学院卡格普尔分校地质与地球物理系）
Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we …
