Search Results - Augmenting Physics-based Models in ICME with Machine Learning AND Uncertainty Quantification~

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials by Hsu, Tim, Epting, William K., Kim, Hokon, Abernathy, Harry W., Hackett, Gregory A., Rollett, Anthony D., Salvador, Paul A., Holm, Elizabeth A.

“…). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution…”
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Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning by Tran, Anh, Wildey, Tim

“…Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process…”
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Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations by Arbabi, Hassan, Bunder, Judith E., Samaey, Giovanni, Roberts, Anthony J., Kevrekidis, Ioannis G.

“…The data-driven discovery of partial differential equations (PDEs) consistent with spatiotemporal data is experiencing a rebirth in machine learning research…”
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Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys by Weber, George, Pinz, Maxwell, Ghosh, Somnath

“…) selection of a PHCPM framework and (5) self-consistent homogenization. Novel machine learning tools are explored at every development phase…”
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Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys by Peng, Jian, Yamamoto, Yukinori, Brady, Michael P., Lee, Sangkeun, Haynes, J. Allen, Shin, Dongwon

“…The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL…”
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Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling by Srinivasan, Sandeep, Swick, Brennan, Groeber, Michael A.

“… We develop a procedure for coupling physics-based process modeling with machine learning and optimization methods to accelerate searching the AM processing space for suitable printing parameter sets…”
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CALPHAD Uncertainty Quantification and TDBX by Lin, Yu, Saboo, Abhinav, Frey, Ramón, Sorkin, Sam, Gong, Jiadong, Olson, Gregory B., Li, Meng, Niu, Changning

“…CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence…”
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Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review by Gabriel, Joshua J., Paulson, Noah H., Duong, Thien C., Tavazza, Francesca, Becker, Chandler A., Chaudhuri, Santanu, Stan, Marius

“… However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges…”
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Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion by Couperthwaite, Richard, Molkeri, Abhilash, Khatamsaz, Danial, Srivastava, Ankit, Allaire, Douglas, Arròyave, Raymundo

“…Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials…”
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Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation by Medghalchi, Setareh, Kusche, Carl F., Karimi, Ehsan, Kerzel, Ulrich, Korte-Kerzel, Sandra

“… In our previous work, we demonstrated that deep learning enables a mechanism-based, statistical analysis by classifying many individual damage sites…”
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Machine Learning of Dislocation-Induced Stress Fields and Interaction Forces by Rafiei, Mohammad H., Gu, Yejun, El-Awady, Jaafar A.

“… The universal approximation theory guarantees the approximation of such functions by some machine learning (ML…”
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Multiscale Modeling of Defect Phenomena in Platinum Using Machine Learning of Force Fields by Chapman, James, Ramprasad, Rampi

“… Recently, machine learning (ML) methods have shown initial promise in bridging these two limitations due to their accuracy and flexibility…”
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Designing a Periodic Table for Alloy Design: Harnessing Machine Learning to Navigate a Multiscale Information Space by Broderick, Scott R., Rajan, Krishna

“… that would not be easily seen otherwise. We embed this machine learning approach with an epistemic uncertainty assessment between data…”
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Expanding Materials Selection Via Transfer Learning for High-Temperature Oxide Selection by McClure, Zachary D., Strachan, Alejandro

“…Materials with higher operating temperatures than today’s state of the art can improve system performance in several applications and enable new technologies…”
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Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks by Venkatraman, Aditya, Montes de Oca Zapiain, David, Kalidindi, Surya R.

“… We present herein a novel machine-learning-based approach for establishing reduced-order models (ROMs…”
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Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows by McDowell, David L.

“… We consider gaps in academic research and education programs related to systems engineering, uncertainty quantification of both experiments…”
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Surrogate Modeling of Viscoplasticity in Steels: Application to Thermal, Irradiation Creep and Transient Loading in HT-9 Cladding by Tallman, Aaron E., Arul Kumar, M., Matthews, Christopher, Capolungo, Laurent

“… Data scarcity creates a need for predictive constitutive models that can be used in regimes outside calibration domains…”
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