Most noninvasive imaging techniques utilize electromagnetic or acoustic waves originating from multiple locations and directions to identify hidden geometrical structures. Surprisingly, it is also possible to image hidden voids and inclusions buried within an object using a single static thermal or mechanical loading experiment by observing the response of the exposed surface of the body, but this problem is challenging to invert. Although physics-informed neural networks (PINNs) have shown promise as a simple-yet-powerful tool for problem inversion, they have not yet been applied to imaging problems with a priori unknown topology. Here, we introduce a topology optimization framework based on PINNs that identifies concealed geometries using exposed surface data from a single loading experiment, without prior knowledge of the number or types of shapes. We allow for arbitrary solution topology by representing the geometry using a material density field combined with a novel eikonal regularization technique. We validate our framework by detecting the number, locations, and shapes of hidden voids and inclusions in many example cases, in both 2D and 3D, and we demonstrate the method's robustness to noise and sparsity in the data. Our methodology opens a pathway for PINNs to solve geometry optimization problems in engineering.
Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
Department of Civil, Environmental, and Infrastructure Engineering, George Mason University（乔治·马歇尔大学土木、环境与基础设施工程系）；State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院寒区工程与冻土科学国家重点实验室，西北生态环境资源研究院）；Laboratoire Navier/CERMES, École Nationale des Ponts et Chaussées, Institut Polytechnique de Paris（巴黎理工学院劳纳实验室/塞梅斯实验室，法国国家桥梁与道路学院）；College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）；School of Geosciences and Info-physics, Central South University（中南大学地球科学与信息物理学院）
University of Calgary（卡尔加里大学）；Carleton University（卡尔顿大学）
In this work, we present a deep neural network architecture that can efficiently approximate classical elasto-plastic constitutive relations. The network is enriched with crucial physics aspects of classical elasto-plasticity, including additive decomposition of strains into elastic and plastic parts, and nonlinear incremental elasticity. This leads to a Physics-Informed Neural Network (PINN) surrogate model named here as Elasto-Plastic Neural Network (EPNN). Detailed analyses show that embedding these physics into the architecture of the neural network facilitates a more efficient training of…
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior knowledge. With the embedded physical laws, PINNs enable the estimation of critical parameters, which are unobservable via physical tools, through observable variables. For example, Power Electronic Converters (PECs) are essential building blocks for the green energy transition. PINNs have been applied to estimate the capacitance, which is unobservable during PEC operations, using current and voltage, which can be ob…
Northeastern University（东北大学）；Beijing University of Technology（北京工业大学）；University of Notre Dame（圣母大学）；Renmin University of China（中国人民大学）；University of Chinese Academy of Sciences（中国科学院大学）
There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data. In specific, the absorbing boundary condition is introduced into the netw…
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. We focus on the problem with a background in elasticity imaging, where one seeks to identify the nonhomogeneous mechanical properties of soft tissue based on the full-field displacement measurements under quasi-static loading. In our model, we apply two independent neural networks, one for approximating the solution of the corresponding forward problem, and the other for approximating the unknown material parameter field. As a proof of concept, we validate our model on a prototypi…
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
George Mason University（乔治梅森大学）；National Institute of Natural Hazards, Ministry of Emergency Management（应急管理部国家自然灾害防治研究院）；Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院西北生态环境资源研究院）；École Nationale des Ponts et Chaussées（巴黎路桥学院）；Hebei University of Engineering（河北工程大学）；Zhejiang University of Technology（浙江工业大学）
Physics-informed neural network for predicting fatigue life of unirradiated and irradiated austenitic and ferritic/martensitic steels under reactor-relevant conditions
Extreme Environments Materials Group (EEMG), Department of Materials Engineering, Indian Institute of Science (IISc)（极端环境材料组（EEMG）、材料工程系、印度科学研究院（IISc））；Department of Materials, University of Oxford（材料系、牛津大学）；Robert Bosch Centre for Cyberphysical Systems, Indian Institute of Science (IISc)（罗伯特·博世网络物理系统中心、印度科学研究院（IISc））
A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
Institute of Structural Analysis, Leibniz Universität Hannover（莱布尼茨汉诺威大学结构分析研究所）；Department of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University（奥斯陆城市大学机械、电气与化学工程系）；Institute of Lightweight Systems, Multifunctional Materials, DLR (German Aerospace Center)（德国航空航天中心轻量化系统与多功能材料研究所）
Oak Ridge National Laboratory（橡树岭国家实验室）；Missouri University of Science and Technology（密苏里科技大学）
For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanical behavior of a material interface undergoing openings, which is critical to understand and predict interfacial failures under complex loadings. However, existing theoretical models have limitations on enough complexity and flexibility to well learn the real-world TSR from experimental observations. A neural network can fit well along with the loading paths but often fails to obey the laws of physics, due to a lack o…
Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data
Department of Industrial and Systems Engineering, Texas A&M University（工业与系统工程系，德克萨斯大学阿马尔科分校）；Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯大学阿马尔科分校）
Siemens AG, Corporate Technology（西门子股份公司，企业技术部）；Technical University of Munich（慕尼黑工业大学）
Identifying accurate dynamic models is required for the simulation and control of various technical systems. In many important real-world applications, however, the two main modeling approaches often fail to meet requirements: first principles methods suffer from high bias, whereas data-driven modeling tends to have high variance. Additionally, purely data-based models often require large amounts of data and are often difficult to interpret. In this paper, we present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to ov…
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
Institute of Applied Mechanics（应用力学研究所）；Technische Universität Braunschweig（Braunschweig技术大学）；Clausthal University of Technology（克莱斯特哈大学）；Institute for Acoustics and Dynamics（声学与动力学研究所）；Eidgenössische Technische Hochschule Zürich（苏黎世联邦理工学院）
Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
Hamburg University of Technology（汉堡工业大学）；Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Universitat Politécnica de Catalunya（加泰罗尼亚理工大学）；China University of Petroleum（中国石油大学）；College of Oceanography（海洋学院）；Space Informatics（空间信息学院）
Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295（波尔多大学、国家科学研究中心、波尔多工业学院、I2M、UMR 5295）；Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、国家科学研究中心、波尔多工业学院、LaBRI、UMR 5800）；Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg（力学与流体动力学研究所、弗赖堡技术大学）；Univ. Grenoble Alpes, CNRS, UMR 5525（格勒诺布尔阿尔卑斯大学、国家科学研究中心、UMR 5525）；Arts et Metiers Institute of Technology, CNRS, Bordeaux INP, I2M, UMR 5295（艺术与工艺技术学院、国家科学研究中心、波尔多工业学院、I2M、UMR 5295）
A recently introduced representation by a set of Wang tiles -- a generalization of the traditional Periodic Unit Cell based approach -- serves as a reduced geometrical model for materials with stochastic heterogeneous microstructure, enabling an efficient synthesis of microstructural realizations. To facilitate macroscopic analyses with a fully resolved microstructure generated with Wang tiles, we develop a reduced order modelling scheme utilizing pre-computed characteristic features of the tiles. In the offline phase, inspired by the computational homogenization, we extract continuous fluctua…
In multi-body dynamics, the motion of a complicated physical object is described as a coupled ordinary differential equation system with multiple unknown solutions. Engineers need to constantly adjust the object to meet requirements at the design stage, where a highly efficient solver is needed. The rise of machine learning-based partial differential equation solvers can meet this need. These solvers can be classified into two categories: approximating the solution function (Physics-informed neural network) and learning the solution operator (Neural operator). The recently proposed physics-inf…
Direct numerical simulation of hierarchical materials via homogenization-based concurrent multiscale models poses critical challenges for 3D large scale engineering applications, as the computation of highly nonlinear and path-dependent material constitutive responses at the lower scale causes prohibitively high computational costs. In this work, we propose a physics-informed data-driven deep learning model as an efficient surrogate to emulate the effective responses of heterogeneous microstructures under irreversible elasto-plastic hardening and softening deformation. Our contribution contain…
Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics
Institute of Metal Forming, RWTH Aachen University（金属成形研究所，亚琛工业大学）；School of Mechanical Engineering, Sichuan University of Science and Engineering（四川轻化工大学机械工程学院）；State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University（西南交通大学轨道交通运载系统全国重点实验室）；School of Mechanical Engineering, Sichuan University（四川大学机械工程学院）
