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RESEARCH PAPER · Computer Methods in Applied Mechanics and Engineering · 2017

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

Miguel Aníbal Bessa, Ramin Bostanabad, Zeliang Liu, A. Hu, Daniel W. Apley, L. Catherine Brinson, Wei-Hao Chen, Wing Kam Liu

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A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionalityOriginalOrigin of notches in CSF: optical or neural?39Adaptive multiscale homogenization of the lattice discrete particle model for the analysis of damage and fracture in concrete61Data-Driven Self-consistent Clustering Analysis of Heterogeneous Materials with Crystal Plasticity60Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network45De novo composite design based on machine learning algorithm470Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials181Leveraging the nugget parameter for efficient Gaussian process modeling65Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing252An uncertainty model of acoustic metamaterials with random parameters53From SEM images to elastic responses: A stochastic multiscale analysis of UD fiber reinforced composites49Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization90Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques434Modeling process-structure-property relationships for additive manufacturing104Estimating mechanical properties from spherical indentation using Bayesian approaches82Homogenization coarse graining (HCG) of the lattice discrete particle model (LDPM) for the analysis of reinforced concrete structures44Uncertainty quantification in multiscale simulation of woven fiber composites130Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials42Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials319Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks192A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites173Hybrid constitutive modeling: data-driven learning of corrections to plasticity models99Wrapping dynamic analysis and optimization of deployable composite triangular rollable and collapsible booms38Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics81Data science for finite strain mechanical science of ductile materials40Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data299Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning133Deep convolutional neural networks for eigenvalue problems in mechanics78Machine Learning Models of Plastic Flow Based on Representation Theory35Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning301Structural-Genome-Driven computing for composite structures59Deep learning–based inverse method for layout design55Self-consistent clustering analysis for multiscale modeling at finite strains87Exploring the 3D architectures of deep material network in data-driven multiscale mechanics197Multiscale computational homogenization of woven composites from microscale to mesoscale using data-driven self-consistent clustering analysis43Transfer learning of deep material network for seamless structure–property predictions58Learning slosh dynamics by means of data49General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning39Derivation of heterogeneous material laws via data-driven principal component expansions100Calibration of DEM material parameters to simulate stress-strain behaviour of unsaturated soils during uniaxial compression64High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach251Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design55MAP123: A data-driven approach to use 1D data for 3D nonlinear elastic materials modeling69A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization89Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network273Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible280A deep energy method for finite deformation hyperelasticity334Bayesian inference of non-linear multiscale 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learning222Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials57A data-driven self-consistent clustering analysis for the progressive damage behavior of 3D braided composites58Inverse-designed spinodoid metamaterials444A new data-driven topology optimization framework for structural optimization55A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths229Homogenized yarn-level cloth69Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures74Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity224Data-driven fracture mechanics149Intelligent layout design of curvilinearly stiffened panels via deep learning-based method59Invited review: Machine learning for materials developments in metals additive manufacturing231Hierarchical 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element implementation93Micro-mechanics and data-driven based reduced order models for multi-scale analyses of woven composites46A methodology to generate design allowables of composite laminates using machine learning113A Stochastic FE2 Data-Driven Method for Nonlinear Multiscale Modeling35CPINet: Parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM39Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties38A supervised machine learning approach for accelerating the design of particulate composites: Application to thermal conductivity39A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths115SINDy-BVP: Sparse identification of nonlinear dynamics for boundary value problems47Recurrent neural network modeling of the large deformation of lithium-ion battery cells69A review of artificial neural networks in the constitutive modeling of composite materials475Data‐Driven Approaches Toward Smarter Additive Manufacturing37Autonomous experimentation systems for materials development: A community perspective398Constrained neural network training and its application to hyperelastic material modeling42Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites38Machine learning based topology optimization of fiber orientation for variable stiffness composite structures44Bayesian neural networks for uncertainty quantification in data-driven materials modeling191A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data45A learning-based multiscale method and its application to inelastic impact problems72Uncertainty modelling and multiscale simulation of woven composite twisted structure48Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel50Data-driven and topological design 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Prediction Methods125Simulation trained CNN for accurate embedded crack length, location, and orientation prediction from ultrasound measurements56The Use of Machine-Learning Techniques in Material Constitutive Modelling for Metal Forming Processes37A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features70Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks48Multi-objective optimization of urban environmental system design using machine learning61A concurrent three-scale scheme FE-SCA 2 for the nonlinear mechanical behavior of braided composites41Equilibrium-based convolution neural networks for constitutive modeling of hyperelastic materials51Eighty Years of the Finite Element Method: Birth, Evolution, and Future322Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials82A State-of-the-Art Review on Machine 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for elastic properties of woven composites58A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials49Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics100Material-Adaptive Anomaly Detection Using Property-Concatenated Transfer Learning in Wire Arc Additive Manufacturing41Properties prediction of composites based on machine learning models: A focus on statistical index approaches51Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review70Advanced computational modelling of composite materials86Artificial neural network enhanced plasticity modeling and ductile fracture characterization of grade-1 commercial pure titanium36Recurrent neural networks and transfer learning for predicting elasto-plasticity in woven composites38A Review on Data-Driven Constitutive Laws for Solids142Machine learning-based constitutive modelling for material non-linearity: A review65Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models42

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