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08/19/2023
Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets
Deep learning (DL) has achieved remarkable successes in many disciplines...
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08/18/2023
Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learning
Advanced Manufacturing (AM) has gained significant interest in the nucle...
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08/04/2023
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning
One predominant challenge in additive manufacturing (AM) is to achieve s...
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07/10/2023
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data
Inverse UQ is the process to inversely quantify the model input uncertai...
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12/16/2022
Roundoff error problem in L2-type methods for time-fractional problems
Roundoff error problems have occurred frequently in interpolation method...
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12/01/2022
Long time H^1-stability of fast L2-1_σ method on general nonuniform meshes for subdiffusion equations
In this work, the global-in-time H^1-stability of a fast L2-1_σ method o...
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11/16/2022
Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks
Artificial Neural Networks (ANNs) have been successfully used in various...
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08/02/2022
On stability and convergence of L2-1_σ method on general nonuniform meshes for subdiffusion equation
In this work the L2-1_σ method on general nonuniform meshes is studied f...
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06/27/2022
Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models
Recent performance breakthroughs in Artificial intelligence (AI) and Mac...
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05/12/2022
H^1-stability of an L2-type method on general nonuniform meshes for subdiffusion equation
In this work the H^1-stability of an L2 method on general nonuniform mes...
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05/11/2021
Bayesian Inverse Uncertainty Quantification of a MOOSE-based Melt Pool Model for Additive Manufacturing Using Experimental Data
Additive manufacturing (AM) technology is being increasingly adopted in ...
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05/02/2021
Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testi
The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems m...
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04/27/2021
A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes
Uncertainty Quantification (UQ) is an essential step in computational mo...
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01/31/2018
Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification
Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the...
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01/28/2018
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Inverse Uncertainty Quantification (UQ) is a process to quantify the unc...
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01/05/2018