Production of high purity 47Sc from proton irradiation of natural vanadium targets
Not Available
Cingoranelli, Shelbie Jaylene↗
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Results for “RiDER”
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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Exploring the consequences and lessons from Underspecification in ML models (MLDL submission).
Abstract not provided.
Rider, William↗
Exploring the consequences and lessons from Underspecification in ML models.
Abstract not provided.
Rider, William↗
Quantifying Uncertainty in Machine Learning Models for Time Series Classification.
Abstract not provided.
Rushdi, Ahmad↗
erification and Validation Using the Null Hypothesis as a Philosophical Grounding.
Abstract not provided.
Rider, William↗
Credibility assessment of machine learning-based surrogate model prediction on NACA 0012 airfoil flow
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Kirsch, Jared Roelof↗
Unraveling Sensitivity and Ensuring Reliability in Reynolds Stress Predictions for Data-Driven RANS
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Balakrishnan, Uma↗
Robust Data-Driven Turbulence Modeling for RANS Closures Using a SciML Approach for Validation
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Balakrishnan, Uma↗
SciML Credibility for Turbulence Closures
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Balakrishnan, Uma [Sandia National Laboratories (S↗
Independent Evaluation and Verification of Data-Driven RANS Closures in Turbulence Modeling
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Balakrishnan, Uma [Sandia National Laboratories (S↗
Explainable Machine Learning for Data-Driven Turbulence Modeling in Compressible Fluid Flows Using SHAP Analysis
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Balakrishnan, Uma [Sandia National Laboratories (S↗
HOLISTIC CREDIBILITY ASSESSMENT OF A MACHINE LEARNING-BASED SURROGATE MODEL IN AN AERODYNAMICS APPLICATION
Elements of ASME V&V 20 credibility assessment methodology are used in tandem with credibility assessment tools from the machine learning community to assess the credibility of a deep neural network based surrogate model. This surrogate model is trained, tested, validated, and employed in the context of aerodynamic coefficient prediction for a NACA 0012 airfoil in subsonic and transonic flow. The parameter space is defined by angle of attack, Reynolds number, and Mach number.
Kirsch, Jared Roelof [Sandia National Laboratories↗
Enhancing Turbulence Modeling for RANS Closures through Explainable Machine Learning Using SHAP Analysis
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Balakrishnan, Uma [Sandia National Laboratories (S↗
Optimized Methods for the Production of High-Purity 203 Pb Using Electroplated Thallium Targets
Not provided.
Radiology, Nuclear Medicine & Medical Imaging↗