4.4 Article

Physics-Informed Multifidelity Residual Neural Networks for Hydromechanical Modeling of Granular Soils and Foundation Considering Internal Erosion

Related references

Note: Only part of the references are listed.
Article Engineering, Geological

Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction

Pin Zhang et al.

Summary: This study proposes a modeling strategy for developing prediction models for soil properties in geotechnical engineering using the Bayesian neural network (BNN) with a strong non-linear fining capability and uncertainty. The results show that BNN can accurately predict compression index and undrained shear strength, but its reliability is low in sparse datasets. Additionally, a novel parametric analysis method is proposed to capture the relationship between input parameters and soil properties.

CANADIAN GEOTECHNICAL JOURNAL (2022)

Article Engineering, Multidisciplinary

A Fourier-based machine learning technique with application in engineering

Michael Peigney

Summary: The article introduces a Fourier-based machine learning method that extends a function into a periodic function and uses partial sums of the Fourier series for approximation. This method can serve as an alternative or complement to neural networks and has some attractive features. In addition to examples of high-dimensional analytical functions, the application to a problem in nonlinear conduction is discussed in detail, along with other examples related to global sensitivity analysis, assessing microstructure effective energies, and solving boundary value problems.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2021)

Review Computer Science, Interdisciplinary Applications

State-of-the-Art Review of Machine Learning Applications in Constitutive Modeling of Soils

Pin Zhang et al.

Summary: Machine learning has shown success in developing constitutive models for soils, with LSTM neural network being the most suitable algorithm. However, research on ML-based soil models is limited, and considerations should be given to data sources and training strategies when developing models.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2021)

Article Engineering, Multidisciplinary

Non-invasive inference of thrombus material properties with physics-informed neural networks

Minglang Yin et al.

Summary: The study utilizes physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data, successfully applied to extract permeability and viscoelastic modulus from thrombus deformation data. The results demonstrate that PINNs can infer material properties from noisy synthetic data and have great potential for inferring these properties from experimental multi-modality and multi-fidelity data.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Engineering, Geological

An offline multi-scale unsaturated poromechanics model enabled by self-designed/self-improved neural networks

Yousef Heider et al.

Summary: This paper presents a meta-modeling approach that utilizes deep reinforcement learning to automatically discover optimal neural network settings for the machine learning constitutive laws. By replacing the human modeler to handle the optimized choices of setup, the AI agent self-learns from taking a sequence of actions within the selection environment. The resulting ML-generated material models can be integrated into a finite element solver to solve initial-boundary-value problems.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2021)

Article Engineering, Geological

A Novel Approach to Surface Strain Measurement for Cylindrical Rock Specimens Under Uniaxial Compression Using Distributed Fibre Optic Sensor Technology

Shao-Qun Lin et al.

Summary: A novel approach utilizing distributed fibre optic sensing technology for surface strain measurement on cylindrical rock specimens under uniaxial compression is proposed. The approach has been validated for accuracy through UCS tests on various materials and shows potential for detecting failure locations and crack development characteristics. Detailed installation procedures and resolution of boundary issues for fibre measurements are provided in the study.

ROCK MECHANICS AND ROCK ENGINEERING (2021)

Article Engineering, Geological

Machine learning-based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application

Pin Zhang et al.

Summary: This study introduces a novel approach by utilizing an artificial neural network with Monte Carlo dropout to correlate soil properties with uncertainty. The proposed model shows excellent performance in predicting accuracy, uncertainty, and monotonicity, which can be applied to simulate the long-term settling and excess pore pressure of an embankment on soft clays.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2021)

Article Engineering, Multidisciplinary

A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning

Jonas Kneifl et al.

Summary: Model order reduction has become a widely used tool for creating efficient surrogate models for time-critical applications. Nonlinear MOR approaches often require intrusive manipulations of simulation code, while nonintrusive MOR approaches using classic model order reduction along with machine learning algorithms have gained attention in recent years. These approaches can provide accurate surrogate models for dynamic mechanical systems, significantly speeding up simulation time while maintaining high-quality state approximations.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2021)

Article Energy & Fuels

Fluid flow through anisotropic and deformable double porosity media with ultra-low matrix permeability: A continuum framework

Qi Zhang et al.

Summary: This study aimed to establish a comprehensive coupled continuum framework to adequately consider the characteristics of fractured porous media or double porosity media. Model applications revealed the framework's capability in capturing the crucial roles of coupling, poroelastic coefficients, anisotropy, and ultra-low matrix permeability in dictating pressure and displacement fields.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2021)

Article Engineering, Multidisciplinary

A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM

Pin Zhang et al.

Summary: This study introduces a novel deep learning-based strategy to accurately identify the mechanical properties and fabric evolutions of granular samples using particle information from photos. By utilizing CNN and BiLSTM neural networks, the study successfully captures the mechanical behaviors and induced fabric evolutions of granular materials.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions

Alexander Fuchs et al.

Summary: This study introduces a meta-modeling framework using artificial intelligence to replicate the path-dependent constitutive responses of composite materials; a Deep Reinforcement Learning combinatorics game is invented to search for optimal hyper-parameter sets automatically; it explores the trade-off between different hyper-parameter configurations and the possibility of transferring hyper-parameter knowledge among different RVEs.

COMPUTERS & STRUCTURES (2021)

Article Computer Science, Interdisciplinary Applications

Multi-fidelity Bayesian neural networks: Algorithms and applications

Xuhui Meng et al.

Summary: A novel class of Bayesian neural networks is proposed for training with noisy data of variable fidelity, applied to function approximations and solving inverse problems based on partial differential equations (PDEs). The multi-fidelity BNNs consist of three neural networks, effectively modeling the correlation and uncertainty between different fidelity data and accurately estimating posterior distributions of hyperparameters. The method demonstrates adaptively capturing linear and nonlinear correlation, identifying unknown parameters in PDEs, and quantifying uncertainties in predictions, ultimately enhancing prediction accuracy through active learning.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

Article Engineering, Geological

BiLSTM-Based Soil-Structure Interface Modeling

Pin Zhang et al.

Summary: The study utilized a deep learning algorithm, bidirectional long short-term memory neural network, to model soil-structure interface behaviors, demonstrating the model's ability in accurately capturing the responses of interface behaviors. Additionally, the model was used to investigate the effects of surface roughness, soil relative density, and normal stiffness on the interface behaviors.

INTERNATIONAL JOURNAL OF GEOMECHANICS (2021)

Review Physics, Applied

Physics-informed machine learning

George Em Karniadakis et al.

Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.

NATURE REVIEWS PHYSICS (2021)

Article Materials Science, Multidisciplinary

Cross-diffusion waves in hydro-poro-mechanics

ManMan Hu et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2020)

Article Engineering, Multidisciplinary

A finite element reduced-order model based on adaptive mesh refinement and artificial neural networks

Joan Baiges et al.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2020)

Article Engineering, Geological

An AI-based model for describing cyclic characteristics of granular materials

Pin Zhang et al.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2020)

Article Computer Science, Interdisciplinary Applications

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

Xuhui Meng et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2020)

Article Engineering, Multidisciplinary

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

Yousef Heider et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

Article Multidisciplinary Sciences

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Lu Lu et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Article Engineering, Marine

A LSTM surrogate modelling approach for caisson foundations

Pin Zhang et al.

OCEAN ENGINEERING (2020)

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

Article Engineering, Geological

Modeling coupled erosion and filtration of fine particles in granular media

Jie Yang et al.

ACTA GEOTECHNICA (2019)

Article Computer Science, Interdisciplinary Applications

Analysis of suffusion in cohesionless soils with randomly distributed porosity and fines content

Jie Yang et al.

COMPUTERS AND GEOTECHNICS (2019)

Article Engineering, Mechanical

Multi-Fidelity Physics-Constrained Neural Network and Its Application in Materials Modeling

Dehao Liu et al.

JOURNAL OF MECHANICAL DESIGN (2019)

Article Materials Science, Multidisciplinary

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

Zeliang Liu et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2019)

Article Engineering, Geological

Internal erosion in dike-on-foundation modeled by a coupled hydromechanical approach

Jie Yang et al.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2019)

Article Physics, Condensed Matter

Statistical Mechanics of Deep Learning

Yasaman Bahri et al.

Annual Review of Condensed Matter Physics (2019)

Article Engineering, Multidisciplinary

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

Kun Wang et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Anuj Karpatne et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)

Article Engineering, Mechanical

Modeling Mechanical Behavior of Very Coarse Granular Materials

Zhen-Yu Yin et al.

JOURNAL OF ENGINEERING MECHANICS (2017)

Article Engineering, Mechanical

Modeling Mechanical Behavior of Very Coarse Granular Materials

Zhen-Yu Yin et al.

JOURNAL OF ENGINEERING MECHANICS (2017)

Article Engineering, Geological

Selection of sand models and identification of parameters using an enhanced genetic algorithm

Yin-Fu Jin et al.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2016)

Article Engineering, Aerospace

Robust Design of a Reentry Unmanned Space Vehicle by Multifidelity Evolution Control

Edmondo Minisci et al.

AIAA JOURNAL (2013)

Article Engineering, Geological

Modeling of fluidsolid interaction in granular media with coupled lattice Boltzmann/discrete element methods: application to piping erosion

Franck Lomine et al.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2013)

Article Computer Science, Interdisciplinary Applications

Bayesian assimilation of multi-fidelity finite element models

F. A. DiazDelaO et al.

COMPUTERS & STRUCTURES (2012)

Article Engineering, Electrical & Electronic

Finite-Element Neural Network-Based Solving 3-D Differential Equations in MFL

Chao Xu et al.

IEEE TRANSACTIONS ON MAGNETICS (2012)

Article Computer Science, Artificial Intelligence

Finite-element neural networks for solving differential equations

P Ramuhalli et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2005)

Article Engineering, Geological

Time for development of internal erosion and piping in embankment dams

R Fell et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2003)

Article Computer Science, Interdisciplinary Applications

Artificial neural network for parameter identifications for an elasto-plastic model of superconducting cable under cyclic loading

M Lefik et al.

COMPUTERS & STRUCTURES (2002)