bayesian-inference

Uncertainty Quantification and Calibration of E3SM Land Model

Poster

Quantifying Uncertainties in Weight-Parameterized Residual Neural Networks

Talk

Reduced-Dimensional Neural Network Surrogate Construction and Calibration of the E3SM Land Model

Talk

Active Learning for SNAP Interatomic Potentials via Bayesian Predictive Uncertainty

Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. The prediction variance is shown to have a strong correlation with …

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Abstract Ground heat flux (G0) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G0 is sparsely measured and not well represented in …

Quantification of Modeling Uncertainty in the Rayleigh Damping Model

Abstract Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper …

Reduced-Dimensional Neural Network Surrogate Construction and Calibration of the E3SM Land Model

Talk

Uncertainty Quantification in Neural Networks

Talk

Visualizing and Quantifying Uncertainty of Physics-aware Neural Networks

Talk

Uncertainty Quantification and Parameter Calibration for High-Dimensional Output Fields of Earth System Models

Poster