Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While-state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used, e.g., climate science, materials science, computational chemistry, and biomedicine.
University of Minnesota（明尼苏达大学）；United States Geologic Survey（美国地质调查局）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimen…
Virginia Tech（弗吉尼亚理工学院暨州立大学）；U.S. Geological Survey（美国地质调查局）；University of Minnesota（明尼苏达大学）
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Further, this framework uses physics-based loss functions in the learning objective of neural networks to ensure that the model predictions not only show lower errors on the training set but are also scientificall…
University of Perugia（佩鲁贾大学）；INFN - Sezione di Perugia（意大利国家核物理研究所佩鲁贾分部）；University "G. D’Annunzio" of Chieti-Pescara（基耶蒂-佩斯卡拉“加布里埃尔·邓南遮”大学）
The continuous growth of data production in almost all scientific areas raises new problems in data access and management, especially in a scenario where the end-users, as well as the resources that they can access, are worldwide distributed. This work is focused on the data caching management in a Data Lake infrastructure in the context of the High Energy Physics field. We are proposing an autonomous method, based on Reinforcement Learning techniques, to improve the user experience and to contain the maintenance costs of the infrastructure.
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…
University of Michigan（密歇根大学）；NOAA/GLERL（美国国家海洋和大气管理局大湖环境研究实验室）
Dangerous large wave put the coastal communities and vessels operating under threats and wave predictions are strongly needed for early warnings. While numerical wave models, such as WAVEWATCH III (WW3), are useful to provide spatially continuous information to supplement in situ observations, however, they often require intensive computational costs. An attractive alternative is machine-learning method, which can potentially provide comparable performance of numerical wave models but only requires a small fraction of computational costs. In this study, we applied and tested a novel machine le…
Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations
University of Pittsburgh（匹兹堡大学）；Aarhus University（奥胡斯大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Maryland（马里兰大学）；Worcester Polytechnic Institute（伍斯特理工学院）
Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
Department of Information Systems, University of Maryland Baltimore County, USA（信息系统系，马里兰大学巴尔的摩县分校）；Department of Earth and Environmental Sciences, KU Leuven, Belgium（地球与环境科学系，鲁汶大学）；Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, USA（大气与海洋科学系，科罗拉多大学博尔德分校）
The formulation of rheological constitutive equations -- models that relate internal stresses and deformations in complex fluids -- is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly str…
PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）
Tsinghua University（清华大学）；Nanjing University of Science and Technology（南京理工大学）；Peking Union Medical College, Chinese Academy of Medical Sciences（中国医学科学院北京协和医学院）
Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availability of digital data on scholarly outcomes offers unprecedented opportunities to explore SciSci. In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research. The researches that choose potentially influential references will have a lead over the emerging publications. Although th…
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
University of Pittsburgh（匹兹堡大学）；University of Alabama（阿拉巴马大学）；University of Minnesota（明尼苏达大学）；Aarhus University（奥胡斯大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Maryland（马里兰大学）
Courant Institute of Mathematical Sciences（库朗数学科学研究所）；New York University（纽约大学）；University of Victoria（维多利亚大学）
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-driven methods to complement traditional numerical simulations methods, motivated by the opportunity to reduce computational costs and/or learn new physical models leveraging access to large collections of data. However, the diversity of problem settings and applications has led to a plethora of approaches, each one evaluated on a different setup and with different evaluation metrics. We introduce a set of benchmark pr…
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
In recent years, the field of machine learning has made phenomenal progress in the pursuit of simulating real-world data generation processes. One notable example of such success is the variational autoencoder (VAE). In this work, with a small shift in perspective, we leverage and adapt VAEs for a different purpose: uncertainty quantification in scientific inverse problems. We introduce UQ-VAE: a flexible, adaptive, hybrid data/model-informed framework for training neural networks capable of rapid modelling of the posterior distribution representing the unknown parameter of interest. Specifica…
Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework
University of Pittsburgh（匹兹堡大学）；University of Alabama（阿拉巴马大学）；University of Minnesota（明尼苏达大学）；Aarhus University（阿arhus大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Maryland（马里兰大学）
