On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems
Aragón Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；University of Zaragoza（萨拉戈萨大学）；Escuela de Ingeniería y Arquitectura (EINA)（工程与建筑学院（EINA））
Jacobo Ayensa-Jiménez, Mohamed H. Doweidar, Jose A. Sanz-Herrera, Manuel Doblaré
Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. The structure of such mathematical models relies on a set of hysical hypotheses that are assumed to be fulfilled by the system within a certain range of environmental conditions. A new perspective is now raising that uses physical knowledge to inform the data prediction capability of artificial neural networks. A particular extension of this data-driven approach is Physically-Guided Neural Networks with Internal Variables (PGNNIV): universal physical laws are used as constraints in the neural network, in such a way that some neuron values can be interpreted as internal state variables of the system. This endows the network with unraveling capacity, as well as better predictive properties such as faster convergence, fewer data needs and additional noise filtering. Besides, only observable data are used to train the network, and the internal state equations may be extracted as a result of the training processes, so there is no need to make explicit the particular structure of the internal state model. We extend this new methodology to continuum physical problems, showing again its predictive and explanatory capacities when only using measurable values in the training set. We show that the mathematical operators developed for image analysis in deep learning approaches can be used and extended to consider standard functional operators in continuum Physics, thus establishing a common framework for both. The methodology presented demonstrates its ability to discover the internal constitutive state equation for some problems, including heterogeneous and nonlinear features, while maintaining its predictive ability for the whole dataset coverage, with the cost of a single evaluation.
Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
Aragón Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；Mechanical Engineering Department（机械工程系）；University of Zaragoza（萨拉戈萨大学）；Institute for Health Research Aragon (IISA)（阿拉贡健康研究所（IISA））；Nanjing Tech University（南京工业大学）；Department of Mechanical Engineering, Universidad Politécnica de Madrid (UPM)（马德里理工大学机械工程系）
Aragón Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；University of Zaragoza（萨拉戈萨大学）；Escuela de Ingeniería y Arquitectura (EINA)（工程与建筑学院（EINA））
Substitution of well-grounded theoretical models by data-driven predictions is not as simple in engineering and sciences as it is in social and economic fields. Scientific problems suffer most times from paucity of data, while they may involve a large number of variables and parameters that interact in complex and non-stationary ways, obeying certain physical laws. Moreover, a physically-based model is not only useful for making predictions, but to gain knowledge by the interpretation of its structure, parameters, and mathematical properties. The solution to these shortcomings seems to be the …
PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems
Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Hong Kong University of Science and Technology（香港理工大学）；Shandong University（山东大学）；University of Oxford（牛津大学）
MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；MIT（麻省理工学院）；Stanford University（斯坦福大学）
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipped with two 9-DOF articulated arms is spawned randomly in a simulated physical home environment. The agent is required to find a small set of objects scattered around the house, pick them up, and transport them to a desired final location. We also position containers around the house that can be used as tools to assist with transporting objects efficiently. To complete the task, an embodied agent must plan a sequence…
Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied to generate crystal materials structures in which the building blocks, the physical atoms are very different from the pixels. Naively transferred generative models tend to generate a large portion of physically infeasible crystal structures that are not stable or synthesizable. Herein we show that by exploiting and adding physically oriented data augmentation, loss function terms, and post processing, our deep advers…
Physically Guided Deep Unsupervised Inversion for 1D Magnetotelluric Models
Universidad Industrial de Santander（桑坦德工业大学）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿产大学）；College of Petroleum Engineering and Geosciences（石油工程与地球科学学院）
Constraint-Guided Learning of Data-driven Health Indicator Models: An Application on the Pronostia Bearing Dataset
KU Leuven（鲁汶大学）；Leuven.AI - KU Leuven institute for AI（鲁汶人工智能研究所（鲁汶大学））；Flanders Make（佛兰德斯制造研究院）；Addis Ababa University（亚的斯亚贝巴大学）；Ghent University（根特大学）
SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics
Department of Mathematics and Scientific Computing, National Institute of Technology Hamirpur（印度国家技术学院哈米尔布尔分校数学与科学计算系）；The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa（海法大学莱昂·H·查尔尼海洋科学学院哈特海洋技术系）
Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
Dept. of Computer Science Georgia State University Atlanta, GA, USA（计算机科学系 佐治亚州立大学）；Dept. of Computer Science California State University Fullerton, CA, USA（计算机科学系 加州州立大学Fullerton分校）
University Paderborn（帕德博恩大学）；Heinz Nixdorf Institute（海因茨·尼克斯多夫研究所）；Paderborn University（帕德博恩大学）
While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments c…
Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation
University of Science and Technology of China（科学技术大学）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；Beijing Jiaotong University（北京交通大学）；Tencent（腾讯）
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching
Centro de Modelamiento Matemático, Universidad de Chile & IRL 2807 - CNRS（智利大学数学建模中心及CNRS IRL 2807）；Department of Chemical Engineering, Biotechnology and Materials, FCFM, Universidad de Chile（智利大学化学工程、生物技术与材料系）；ANID - Millenium Science Initiative, Millenium Nuclei of Advanced MXenes for Sustainable Applications (AMXSA)（ANID-千年科学计划，可持续应用先进MXenes的千年核）；CSIR-National Physical Laboratory, Dr. K.S. Krishnan Marg, New Delhi, 110012, India（印度国家物理实验室，Dr. K.S. Krishnan Marg，新德里，110012）
Virginia Tech（弗吉尼亚理工大学）；U.S. Geological Survey（美国地质调查局）
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effect…
