Výsledky vyhledávání - "Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification"

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

“…Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate…”
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Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning Autor 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…”
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Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations Autor 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 Autor Weber, George, Pinz, Maxwell, Ghosh, Somnath

“…This article establishes a multiscale modeling framework for the parametrically homogenized crystal plasticity model (PHCPM) for single crystal Ni-based…”
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Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling Autor Srinivasan, Sandeep, Swick, Brennan, Groeber, Michael A.

“…Laser powder bed fusion additive manufacturing (AM) is a highly active research area in the materials and manufacturing community, driven by promises of…”
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Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion Autor 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 Autor Medghalchi, Setareh, Kusche, Carl F., Karimi, Ehsan, Kerzel, Ulrich, Korte-Kerzel, Sandra

“…Microstructural damage can occur during metal forming, but how and where this happens vary with the local microstructure and strain path. Large-scale analysis…”
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Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys Autor 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…”
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Multiscale Modeling of Defect Phenomena in Platinum Using Machine Learning of Force Fields Autor Chapman, James, Ramprasad, Rampi

“…Computational methodologies have been critical to our understanding of defects at nanometer scales. These methodologies have been dominated by two classes:…”
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Machine Learning of Dislocation-Induced Stress Fields and Interaction Forces Autor Rafiei, Mohammad H., Gu, Yejun, El-Awady, Jaafar A.

“…In discrete dislocation dynamics (DDD) simulations dislocation-induced stress fields and dislocation–dislocation interaction forces are typically evaluated…”
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Expanding Materials Selection Via Transfer Learning for High-Temperature Oxide Selection Autor 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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Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review Autor Gabriel, Joshua J., Paulson, Noah H., Duong, Thien C., Tavazza, Francesca, Becker, Chandler A., Chaudhuri, Santanu, Stan, Marius

“…The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to…”
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Designing a Periodic Table for Alloy Design: Harnessing Machine Learning to Navigate a Multiscale Information Space Autor Broderick, Scott R., Rajan, Krishna

“…We provide an overview of how to apply statistical learning methods to directly track the role of alloying additions in the multiscale properties of alloys…”
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CALPHAD Uncertainty Quantification and TDBX Autor 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. We report a framework and software packages to enable…”
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Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks Autor Venkatraman, Aditya, Montes de Oca Zapiain, David, Kalidindi, Surya R.

“…The design and development of materials with increased damage resilience is often impeded by the difficulty in establishing the precise linkages, with…”
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Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows Autor McDowell, David L.

“…Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the…”
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Surrogate Modeling of Viscoplasticity in Steels: Application to Thermal, Irradiation Creep and Transient Loading in HT-9 Cladding Autor Tallman, Aaron E., Arul Kumar, M., Matthews, Christopher, Capolungo, Laurent

“…The use of structural metals in extreme environments relies both on the characterization of the mechanical response and microstructure changes in service and…”
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