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RESEARCH PAPER · Computer Methods in Applied Mechanics and Engineering · 2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Gregory H. Teichert, Krishna C. Garikipati

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Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamicsOriginalThe evolving landscape for alloy design29A graph theoretic framework for representation, exploration and analysis on computed states of physical systems12Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions108Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise77Interpreting stochastic agent-based models of cell death11Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences639A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves58Multiscale modeling meets machine learning: What can we learn?353Mechanical MNIST: A benchmark dataset for mechanical metamodels73Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio21System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-1932Multi-fidelity and multi-objective optimization of low-thrust transfers with control strategy for all-electric geostationary satellites23Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys29Machine 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 hyperelasticity224Geometric Stability Classification: Datasets, Metamodels, and Adversarial Attacks9Exploring the potential of transfer learning for metamodels of heterogeneous material deformation21Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization37Deep Learning for Computational Mechanics2Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods230Bayesian neural networks for weak solution of PDEs with uncertainty quantification14Pathways to controlled 3D deformation of graphene: Manipulating the motion of topological defects7Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials46Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges238A classification-based fuzzy-rules proxy model to assist in the full model selection problem in high volume datasets5Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys1Surrogate-adjoint refine based global optimization method combining with multi-stage fuzzy clustering space reduction strategy for expensive problems3System Inference Via Field Inversion for the Spatio-Temporal Progression of Infectious Diseases: Studies of COVID-19 in Michigan and Mexico9Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Deep learning approach for chemistry and processing history prediction from materials microstructure22A heteroencoder architecture for prediction of failure locations in porous metals using variational inference.5mechanoChemML: A software library for machine learning in computational materials physics5Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space92Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data17Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model11Enhancing Mechanical Metamodels With a Generative Model-Based Augmented Training Dataset19Machine-learning-based surrogate modeling of microstructure evolution using phase-field61Learning two-phase microstructure evolution using neural operators and autoencoder architectures162Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition9Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty7Physics-informed convolutional neural networks for temperature field prediction of heat source layout without labeled data153Computational Mechanics with Deep Learning0Design of Light-Metal Alloys Using Machine Learning Techniques0Deep Learning of Microstructures0A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures19Conceptual Foundations of Multi-Fidelity Materials AI — Assumptions and Trade-Offs: A Review Study0Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network43Accelerating the design of compositionally complex materials via physics-informed artificial intelligence110Deep Learning Model to Predict Ice Crystal Growth17Understanding and design of metallic alloys guided by phase-field simulations236Fundamentals of Mesoscale Simulation Methods0Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy16Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models20The emergent role of explainable artificial intelligence in the materials sciences32An accelerated scheme for solving inhomogeneous elasticity in phase-field method and application to Ni-based multi-component alloys0Exploring the Integration of Informed Machine Learning in Engineering Applications: A Comprehensive Review3Inverse Modeling and System Inference from Data0Reduced-Order Models: Numerical Homogenization for the Elastic Response of Material Microstructures0Scale Bridging0Surrogate Optimization0Exploring the Integration of Informed Machine Learning in Engineering Applications: A Comprehensive Review41A thermodynamically consistent machine learning-based finite element solver for phase-field approach12Review of progress in calculation and simulation of high-temperature oxidation181Tutorials: Physics-informed machine learning methods of computing 1D phase-field models6Mechanochemistry: Fundamental Principles and Applications92Multicrystalline informatics: a methodology to advance materials science by unraveling complex phenomena7Deep Learning and Machine Learning for Materials Design3Graph Laplacian-based Bayesian multi-fidelity modeling2Study on the Dynamic Mechanical Damage Behavior of Concrete Based on the Phase-Field Model0Multifidelity Bayesian Optimization: A Review23Importance of Hyper-Parameter Optimization During Training of Physics-Informed Deep Learning Networks6SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method8Accelerating phase-field simulation of coupled microstructural evolution using autoencoder-based recurrent neural networks5Deep learning for simulating the evolution of condensed matter systems at the continuum scale: methods and applications1Machine learning-based surrogate modeling for fast prediction of porosity evolution in U3Si2 nuclear fuel under irradiation0Graph representation learning for predicting processing conditions in Spinodal decomposition microstructures1From multiphysics modeling to AI-Driven simulation: A review of CVI processes for carbon/carbon composites0Integrated PredRNN++ and phase-field for microstructure prediction and simulation acceleration1Accelerating phase-field prediction of grain growth in strongly anisotropic systems using a CNN-transformer surrogate model1Artificial intelligence for advanced alloy: multiscale simulation, intelligent manufacturing, high-throughput characterization, and service performance intelligent assessment0

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  3. Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

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  4. Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise

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  5. Interpreting stochastic agent-based models of cell death

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  9. Mechanical MNIST: A benchmark dataset for mechanical metamodels

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  10. Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio

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  11. System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

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  15. Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity

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  16. Geometric Stability Classification: Datasets, Metamodels, and Adversarial Attacks

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  17. Exploring the potential of transfer learning for metamodels of heterogeneous material deformation

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  18. Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization

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  21. Bayesian neural networks for weak solution of PDEs with uncertainty quantification

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  39. Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition

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