Bayesian Inference

From Theory to Application: A Practical Introduction to Neural Operators in Scientific Computing
This focused review explores a range of neural operator architectures for approximating solutions to parametric partial differential …
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Neural Networks to Accelerate Scientific Computing
Development and application of neural networks to accelerate scientific computing in areas of mechanistic simulation, parameter estimation, model selection, and optimization of materials and structures.
Neural Networks to Accelerate Scientific Computing
Goal-Oriented A-Posteriori Estimation of Model Error as an Aid to Parameter Estimation
In this work, a Bayesian model calibration framework is presented that utilizes goal-oriented a-posterior error estimates in quantities …
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BayesForSEIRD
Calibration of SEIRD model under uncertainty: Application of Bayesian statistics
Feb 9, 2021
Bayesian-Based Predictions of COVID-19 Evolution in Texas Using Multispecies Mixture-Theoretic Continuum Models
We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial …
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