Identification of state functions by physically-guided neural networks with physically-meaningful internal layers
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 Antonio Sanz-Herrera, Manuel Doblaré
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 seamless blending of the tremendous predictive power of the data-driven approach with the scientific consistency and interpretability of physically-based models. We use here the concept of physically-constrained neural networks (PCNN) to predict the input-output relation in a physical system, while, at the same time fulfilling the physical constraints. With this goal, the internal hidden state variables of the system are associated with a set of internal neuron layers, whose values are constrained by known physical relations, as well as any additional knowledge on the system. Furthermore, when having enough data, it is possible to infer knowledge about the internal structure of the system and, if parameterized, to predict the state parameters for a particular input-output relation. We show that this approach, besides getting physically-based predictions, accelerates the training process, reduces the amount of data required to get similar accuracy, filters partly the intrinsic noise in the experimental data and provides improved extrapolation capacity.
University of Sheffield（谢菲尔德大学）；Vattenfall Research and Development（大瀑布电力研发公司）
A wind turbines' power curve is easily accessible damage sensitive data, and as such is a key part of structural health monitoring in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty propagation analysis. Many probabilistic power curve models have a key limitation in that they are not physically meaningful - they return mean and uncertainty predictions outside of what is physically possible (the maximum and minimum power…
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into healthcare. While this promises improved detection and monitoring of breathing disorders, AI techniques are almost invariably "data hungry" which highlights the importance of generating physically meaningful surrogate data. Indeed, domain aware surrogates would enable both an improved understanding of respiratory waveform changes with different breathing disorders, and enhance the training of machine…
From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯·斯特尔特大学计算、数学与工程学院）；Department of Computer Science, Rensselaer Polytechnic Institute（伦斯勒理工学院计算机科学系）
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…
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））
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 Varia…
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）
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）
Georgia Tech（佐治亚理工学院）；NVIDIA（英伟达）；Univ. of Utah（犹他大学）；Univ. of Washington（华盛顿大学）
Geometric organization of objects into semantically meaningful arrangements pervades the built world. As such, assistive robots operating in warehouses, offices, and homes would greatly benefit from the ability to recognize and rearrange objects into these semantically meaningful structures. To be useful, these robots must contend with previously unseen objects and receive instructions without significant programming. While previous works have examined recognizing pairwise semantic relations and sequential manipulation to change these simple relations none have shown the ability to arrange obj…
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…
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）
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）
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine learning models have been proposed to address this problem. In this work, we introduce strategies to improve local explanations taking into account the distribution of the data used to train the black-box models. We show that our approach, MeLIME, produces more meaningful explanations compared to other techniques over different ML models, operating on various types of data. MeLIME generalizes the LIME method, allowin…
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）
Explanation-Guided Adversarial Training for Robust and Interpretable Models
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Fuzhou University（福州大学）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（神经网络与模式识别实验室、机器学习与智能系统研究所、自动化研究所，中国科学院）
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…
PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations
Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mineral Resources（共同科学与工业研究组织（CSIRO）矿产资源部）；Department of Mathematics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校数学系）；Commonwealth Scientific and Industrial Research Organisation (CSIRO) Data61（共同科学与工业研究组织（CSIRO）Data61）；School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（Curtin大学电气工程、计算与数学科学学院）；Department of Electrical and Electronic Engineering, BRAC University（BRAC大学电子与电气工程系）
Neural Diffusion Processes for Physically Interpretable Survival Prediction
Department of Computing Sciences, Bocconi University, Milano, Italy（博科尼大学计算科学系，米兰，意大利）；Computational Biomedicine Unit, University of Torino, Torino, Italy（都灵大学计算生物医学单元，都灵，意大利）
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）
Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts s…
Universidad Autónoma de Baja California（下加利福尼亚自治大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-dom…
