Computational tools for inversion and uncertainty estimation in respirometry

Data and Model Uncertainty Estimation for Linear Inversion

Inverse theory concerns the problem of making inferences about physical systems from indirect noisy measurements. Information about the errors in the observations is essential to solve any inverse problem, otherwise it is impossible to say when a feature “fits the data.” In practice, however, one seldom has a direct estimate of the data errors. We exploit the trade-off between data prediction a...

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation: Final report

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation

Estimation of seabed geoacoustic parameters in shallow water by remote sensing remains a challenging task due to constraints on hardware, data collection and analysis, and cost of maritime surveys. This work focuses on the application of two techniques that might offer a solution to those constraints: the use of ambient noise to probe the seabed, and Bayesian inversion of these data to estimate...

Uncertainty in fundamental natural frequency estimation for alluvial deposits

Seismic waves are filtered as they pass through soil layers, from bedrock to surface. Frequencies and amplitudes of the response wave are affected due to this filtration effect and this will result in different ground motion characteristics. Therefore, it is important to consider the impact of the soil properties on the evaluation of earthquake ground motions for the design of structures. Soil ...

Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics

Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. ¥_rification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically prov...