VVUQ 70 VERIFICATION, VALIDATION, AND UNCERTAINTY QUANTIFICATION OF MACHINE LEARNING
 
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Charter
Coordinate, promote, and foster the development of standards that provide procedures for assessing and quantifying the credibility of machine learning algorithms applied to mechanistic and process modeling. 


Officers Staff Contact
CHAIR Katherine E. Lewis
STAFF SECRETARY Kathryn Hyam, PE
STAFF SECRETARY Daniel Papert
Kathryn Hyam, PE
The American Society of Mechanical Engineers
Two Park Avenue
New York, NY 10016
Phone: 1(212)591-8704
Daniel Papert
The American Society of Mechanical Engineers
Two Park Avenue
New York, NY 10016
Phone: 1(212)591-7526
 
Additional Committees
VVUQ 10 Verification, Validation, and Uncertainty Quantification in Computational Solid Mechanics
VVUQ 20 Verification, Validation, and Uncertainty Quantification in Computational Fluid Dynamics and Heat Transfer
VVUQ 30 Verification, Validation, and Uncertainty Quantification in Computational Simulation of Nuclear System Thermal Fluids Behavior
VVUQ 40 Verification, Validation, and Uncertainty Quantification in Computational Modeling of Medical Devices
VVUQ 50 Verification, Validation, and Uncertainty Quantification of Computational Modeling for Advanced Manufacturing
VVUQ 60 Verification, Validation, and Uncertainty Quantification of Computational Modeling in Energy Systems
VVUQ Verification, Validation, and Uncertainty Quantification in Computational Modeling and Simulation
 
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