Publications
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Jump to search filtersInformation Extraction and Logical Inference for Derivative Classifier Assistance
Abstract not provided.
Neurogenesis Deep Learning
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Accelerating LAMMPS Performance
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Block Preconditioning Approaches to Compatible Electromagnetic and Plasma Discretizations
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UQ Theories Principles and Tools: Dakota Topics
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Welcome and What's New in LAMMPS
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A Quick Tour of LAMMPS
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Algebraic multigrid solvers for thin-domain problems: application to ice sheet modeling
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Numerical Libraries: Community Achievements Challenges and Opportunities
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Warm dense matter: opportunities and challenges for TDDFT
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Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures
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Spin--lattice simulations with LAMMPS
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Spin--lattice simulations with LAMMPS
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Beyond BUS-PASS
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Modeling the Effects of Microstructure on Localization in Polycrystalline Stainless Steel
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Summer 2016 Project
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Modeling Microstructure Evolution During Metal Additive Manufacturing
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Visual Cognition: Human Comprehension of Data Visualizations
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miniTri ? Triangle Based Data Analytics Miniapp
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Neural Computing for Scientific Computing Applications:
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Sparta-Catalyst Integration Progress in ATDM (Highlight Slide)
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Record Use of ParaView at Sandia National Laboratories
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A Classical MD Primer
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Tackling UQ in DARMA a Programming Model for Task-Based Execution at Extreme-Scale
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Information Extraction and Logical Inference for Derivative Classifier Assistance
Abstract not provided.
Integration of Dakota into the NEAMS Workbench
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on integrating Dakota into the NEAMS Workbench. The NEAMS Workbench, developed at Oak Ridge National Laboratory, is a new software framework that provides a graphical user interface, input file creation, parsing, validation, job execution, workflow management, and output processing for a variety of nuclear codes. Dakota is a tool developed at Sandia National Laboratories that provides a suite of uncertainty quantification and optimization algorithms. Providing Dakota within the NEAMS Workbench allows users of nuclear simulation codes to perform uncertainty and optimization studies on their nuclear codes from within a common, integrated environment. Details of the integration and parsing are provided, along with an example of Dakota running a sampling study on the fuels performance code, BISON, from within the NEAMS Workbench.
SST Tutorial 2017 - Juno Example Processor
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Robust Verification of the Multi-Fluid Plasma Model in Drekar
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Quantum Approximation Algorithms
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Milestone Completion Report WBS 1.3.5.05 ECP/VTK-m FY17Q3 [MS-17/02] Faceted Surface Normals STDA05-3
The FY17Q3 milestone of the ECP/VTK-m project includes the completion of a VTK-m filter that computes normal vectors for surfaces. Normal vectors are those that point perpendicular to the surface and are an important direction when rendering the surface. The implementation includes the parallel algorithm itself, a filter module to simplify integrating it into other software, and documentation in the VTK-m Users’ Guide. With the completion of this milestone, we are able to necessary information to rendering systems to provide appropriate shading of surfaces. This milestone also feeds into subsequent milestones that progressively improve the approximation of surface direction.
Towards a Multi-fidelity Hemodynamic Model Pipeline for the Analysis of Cardiovascular Flow Under Uncertainty
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Preliminary Application of Neural Networks to Support Assessment of Ground-Based Imagery for International Safeguards Analysis
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Multilevel-Multifidelity Expansions with Application to Forward UQ OUU and Emulator-Based Bayesian Inference
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Progress toward 3D Extended MHD modeling in an ALE Framework in ALEGRA
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Corroborating Tomographic Defect Metrics with Processing Parameters & Mechanical Response in Metal AM
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Stabilization of Peridynamic Correspondence Material Models
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Beyond Hugoniots: seeking consistency in observables
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Extended Math Programming as a framework for CPS models and analysis
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Design Optimization of Materials with Tailored Impedance for Vibration Control
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FrogEye: Joint analysis of source code and binaries using Machine Learning
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An Agile Computational Approach to Crystal Plasticity
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Uncertainty Aware Topology Optimization Via a Stochastic Reduced Order Model Approach
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Sampling Polynomial Chaos and Functional Tensor Train Multilevel/Multifidelity Strategies for Forward UQ
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Integration of Mesh Adaptivity with a High-Deformation Multi-Material Simulation
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Model Discrepancy Calibration Across Experiments
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Experiences in Automated Calibration of a Nickel Equation of State
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Additive manufacturing: Toward holistic design
Scripta Materialia
Additive manufacturing offers unprecedented opportunities to design complex structures optimized for performance envelopes inaccessible under conventional manufacturing constraints. Additive processes also promote realization of engineered materials with microstructures and properties that are impossible via traditional synthesis techniques. Enthused by these capabilities, optimization design tools have experienced a recent revival. The current capabilities of additive processes and optimization tools are summarized briefly, while an emerging opportunity is discussed to achieve a holistic design paradigm whereby computational tools are integrated with stochastic process and material awareness to enable the concurrent optimization of design topologies, material constructs and fabrication processes.
Improving Wind Power Prediction Intervals Using Vendor-Supplied Probabilistic Forecast Information
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Solving the Mixed Integer Non-linear Programming Problem of Unit Commitment
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