Related papers: Uncertainty quantification for CO2 sequestration a…
The high accuracy of large-scale weather forecasting models like Aurora is often accompanied by a lack of transparency, as their internal representations remain largely opaque. This "black box" nature hinders their adoption in high-stakes…
Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate and robust monitoring system to ensure the effectiveness of…
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to…
Underground hydrogen storage (UHS) in geological formations is a promising technology for large-scale hydrogen energy storage. Although lessons were learned from similar studies, including geological carbon sequestration and underground gas…
Unraveling the adsorption mechanism and thermodynamics of O$_2$ and H$_2$O on uranium dioxide surfaces is critical for the nuclear fuel storage and uranium corrosion. Based on the first-principles DFT+U-D3 calculations, we carefully test…
Quantifying and reducing uncertainty in Earth system model parameterizations is essential to improving their reliability in decision-making. Forward uncertainty propagation is used to derive parameter sensitivity but requires physically…
Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly…
Caprock integrity is one of the chief concerns in the successful development of a CO$_2$ storage site. In this chapter, we provide an overview of the permeability of fractured shale, the potential for mitigation of CO$_2$ leakage by…
We present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies. We then provide a comprehensive and statistically consistent framework…
This study proposes a novel approach to quantifying uncertainties of constitutive relations inferred from noisy experimental data using inverse modelling. We focus on electrochemical systems in which charged species (e.g., Lithium ions) are…
Predictive modeling of CO2 storage sites requires a detailed understanding of physico-chemical processes and potential challenges for scale-up. Dramatic injectivity decline may occur due to salt precipitation pore clogging in high-salinity…
How to accurately quantify the uncertainty of stochastic dynamical responses affected by uncertain loads and structural parameters is an important issue in structural safety and reliability analysis. In this paper, the conditional…
Uncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As large-scale modeling is typically performed with simplified…
The purpose of this study is twofold -- first, to introduce the application of high-order discontinuous Galerkin methods to buoyancy-driven cargo hold fire simulations, second, to explore statistical variation in the fluid dynamics of a…
A discretely fractured reservoir has the capability to support and sustain long term geothermal energy extraction operations. The process of injecting cold water to extract hot water from a fracture reservoir results in thermal and…
Metal-organic frameworks (MOFs) with ultra-small pores offer an optimal environment to effectively capture guest molecules such as CO2. Subtle local dynamics of their frameworks, either throughout reorientation of functional groups grafted…
Atomistic simulations often rely on interatomic potentials to access greater time- and length- scales than those accessible to first principles methods such as density functional theory (DFT). However, since a parameterised potential…
The uncertainty principle is an inherent characteristic of quantum mechanics. This principle can be formulated in various form. Fundamentally, this principle can be expressed in terms of the standard deviation of the measured observables.…
Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios,…
This study presents a Bayesian framework for (inverse) uncertainty quantification and parameter estimation in a two-step Chemical Vapor Deposition coating process using production data. We develop an XGBoost surrogate model that maps…