Machine learned constitutive models for foam mechanics and powder rheology.
Abstract not provided.
Bolintineanu, Dan Stefan↗
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Results for “machine learning models”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Machine learned surrogate models for threaded fastener geometries in finite element analysis.
Abstract not provided.
Mersch, John↗
Assembling training datasets for generalizable machine-learned turbulence models.
Abstract not provided.
Ray, Jaideep↗
Assembling training datasets for generalizable machine-learned turbulence models.
Abstract not provided.
Ray, Jaideep↗
Efficient Sampling Methods for Machine Learning Error Models with application to Surrogates of Steady Hypersonic Flows.
Abstract not provided.
Krath, Elizabeth↗
Optimization of Completion Design and Well Spacing with Complex Fracture Modeling and Machine Learning Techniques.
Abstract not provided.
Yoon, Sangcheol↗
A Physics-based Reduced Order Model with Machine Learning Boosted Hyper-Reduction.
Abstract not provided.
Konstantinos, Vlachas↗
Expediting the materials discovery process of MPEAs through atomistic modeling and machine learning techniques.
Abstract not provided.
Startt, Jacob↗
Machine learning constitutive models of inelastic materials with microstructure.
Abstract not provided.
Jones, Reese↗
Estimating Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models.
Abstract not provided.
Wildey, Timothy↗
Machine learning constitutive models of inelastic materials with microstructure.
Abstract not provided.
Jones, Reese↗
Machine-Learned Surrogate Models for Threaded Fastener Geometries Subjected to Multiaxial Loadings.
Abstract not provided.
Mersch, John↗
Machine-Learned Surrogate Models for Threaded Fasteners and their Application in Solid Mechanics Finite Element Analysis
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Mersch, John Philip↗
The LAMMPS particle simulation package: Bringing together innovative physics models, machine-learning interatomic potentials, and extreme-scale computing resources
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Thompson, Aidan P.↗
Complementing the CCS Class VI Well Permit Process with DOE-NETL's SMART Initiative Tools and Workflows
This is a presentation on model explorer developed under SMART initiative Task 2. Our team will present the current progress of the model explorer in using machine learning models to accelerate CCS project at GWPC meeting. Model explorer bring new capabilities, (fast, Realtime, and accurate) that can help CCS stakeholders including regulatory agencies, public and site operators make faster decisions and process information and data.
Hosseini, Seyyed↗
Machine-learned ACE models with charge equilibration in LAMMPS (workshop)
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Goff, James Michael↗
Machine learning constitutive models of inelastic materials with microstructure
Explore the source record for details and available documents.
Jones, Reese Edward [Sandia National Laboratories ↗