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Article
Mechanics
Luhang Shen et al.
Summary: This paper proposes an approximation-correction model to solve unsteady compressible seepage equations without using any labeled data. The model contains two neural networks, one for approximating the asymptotic solution and the other for correcting the error of the approximation. Numerical experiments show that the proposed method can solve seepage equations with high accuracy, which is a significant breakthrough for deep learning-based methods to solve PDEs.
Article
Computer Science, Interdisciplinary Applications
Michael Penwarden et al.
Summary: Physics-informed neural networks (PINNs) are gaining attention for discretizing partial differential equations (PDEs) in Computational Science and Engineering (CS&E). PINNs face challenges in terms of accuracy, convergence, optimization strategies, and computational cost. This paper introduces metalearning concepts to speed up PINNs optimization and tests the approach on various parameterized PDEs.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Mechanical
Ruihua Liang et al.
Summary: This paper proposes a new PINN frequency domain (PINNFD) method by embedding Fourier features to solve the difficulty of approximating multi-frequency target functions in PINN. The effectiveness of the proposed method is validated by solving elastodynamics problems under various dynamic point loads. The results show that the proposed PINNFD method achieves better results in all cases of loading conditions, demonstrating its advancement in solving multi-frequency problems in engineering applications.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
C. Dhanamjayulu et al.
Summary: This research investigates ways to identify malnutrition affected people and obese people by analyzing body weight and BMI from facial images, proposing a regression method and employing Convolutional Neural Networks. The focus is on evaluating BMI from facial images to create a system for real-time assessment of individuals' health status.
IET IMAGE PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Pao-Hsiung Chiu et al.
Summary: This study proposes a novel physics-informed neural network (PINN) method that combines automatic differentiation (AD) and numerical differentiation (ND) to improve the training efficiency and accuracy. The proposed method, called canPINN, achieves more robust and efficient training than AD-based PINNs, while improving accuracy by up to 1-2 orders of magnitude relative to ND-based PINNs. The method is demonstrated on challenging fluid dynamic problems, showing consistently high accuracy compared to conventional AD-based PINNs.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Jeremy Yu et al.
Summary: The article introduces a new method called gradient-enhanced physics-informed neural networks (gPINNs) to improve the accuracy of PINNs. gPINNs leverage the gradient information of the PDE residual to enhance the loss function. Experimental results demonstrate that gPINNs outperform traditional PINN with fewer training points.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Mechanical
Bin Zheng et al.
Summary: This study focuses on using physically-informed neural networks to simulate the crack propagation of quasi-brittle materials under complex loading. By minimizing energy, we use neural networks to predict crack propagation while maintaining thermodynamic consistency. The results show that our proposed method accurately predicts displacement fields under different loading conditions.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2022)
Article
Energy & Fuels
Zhao Zhang
Summary: The physics-informed neural network (PINN) is a general deep learning framework for simulating physical processes and surrogate modeling without labeled data. In this study, a physics informed deep convolutional neural network (PIDCNN) architecture is proposed for simulating and predicting transient Darcy flows in highly heterogeneous reservoir models. Test cases show that PIDCNN can accurately simulate flows and serve as a surrogate model for predicting flow fields in unknown reservoirs.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Energy & Fuels
Luhang Shen et al.
Summary: This paper proposes a physics-informed method to solve a porous flow equation using neural networks, achieving higher accuracy and allowing for the inversion of permeability from measurable data.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Miao Rong et al.
Summary: The Lagrangian dual-based TgNN (TgNN-LD) is proposed to improve the effectiveness of the training process by converting the original loss function into a constrained form, incorporating partial differential equations, engineering controls, and expert knowledge as constraints, and achieving a fair trade-off between observation data and constraints to improve prediction accuracy and training efficiency.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Shaojie Zeng et al.
Summary: This paper presents three adaptive techniques for improving the computational performance of deep neural network (DNN) methods for high-dimensional partial differential equations (PDEs). These techniques include adaptive choice of loss function, adaptive activation function, and adaptive sampling. Numerical experiments have shown that these adaptive techniques significantly improve computational accuracy and accelerate convergence speed.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Zhiwei Fang
Summary: The article introduces a hybrid physics-informed neural network (hybrid PINN) for solving partial differential equations (PDEs) by using an approximation of the differential operator. This method has a convergent rate, avoiding the issue of bad predictions by neural networks. It is the first work to have a machine learning PDE solver with a convergent rate like numerical methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Energy & Fuels
Wenshu Zha et al.
Summary: This paper introduces a novel method for reconstructing digital shale core samples based on GANs, which successfully generates realistic digital core samples. Evaluation metrics like FID and KID demonstrate that the real and reconstructed samples are highly similar, indicating the effectiveness of the new reconstruction method.
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
(2021)
Article
Multidisciplinary Sciences
Xinhai Chen et al.
Summary: This paper introduces an improved data-free surrogate model, DFS-Net, based on deep neural networks, which uses a weighting mechanism and neural network structure to address the instability or inaccuracy in predicting PDEs. Experimental results demonstrate that DFS-Net achieves a good trade-off between accuracy and efficiency.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Multidisciplinary
Rishikesh Ranade et al.
Summary: This work aims to develop an ML-based PDE solver that combines important characteristics of existing PDE solvers with ML technologies. By using discretization and iterative algorithms, the ML-solver can achieve good accuracy, stability, and faster convergence in solving highly non-linear, coupled PDE solutions.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Luning Sun et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Basemah Alshemali et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Engineering, Civil
Nanzhe Wang et al.
JOURNAL OF HYDROLOGY
(2020)
Article
Computer Science, Interdisciplinary Applications
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Biochemical Research Methods
Yu Li et al.
Article
Computer Science, Artificial Intelligence
S. Zagoruyko et al.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2017)
Article
Energy & Fuels
Daolun Li et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2016)
Article
Engineering, Petroleum
Daolun Li et al.
Article
Energy & Fuels
Daolun Li et al.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2016)
Article
Energy & Fuels
Daolun Li et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2014)
