UM-Bridge: Uncertainty quantification and modeling
bridge

Seelinger, Linus; Cheng-Seelinger, Vivian; Davis, A. D.; Parno, Matthew; Reinarz, Anne

doi:10.21105/joss.04748

Cited by 3 publications

(2 citation statements)

References 10 publications

(10 reference statements)

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“…We use the AM‐MCMC implementation provided by the open‐source Massachusetts Institute of Technology Uncertainty Quantification Library (MUQ, Parno et al., 2021). To couple MUQ to the forward model in the ExaHyPE simulation framework, we use the universal UQ/model interface UM‐Bridge (Seelinger et al., 2023), which is fully supported by MUQ. For reproducibility, we provide, in the open research section, the forward model as a ready‐to‐run container image that any UM‐Bridge supporting UQ software can connect to.…”

Section: Methodsmentioning

confidence: 99%

“…The combination of the AM‐MCMC algorithm of MUQ (Parno et al., 2021) and the forward modeling in ExaHyPE (Reinarz et al., 2020) is implemented with UM‐Bridge (Seelinger et al., 2023). The detailed description of these packages, the algorithms therein, as well as the code of our implementation are provided in the following repository: https://zenodo.org/badge/latestdoi/551506661.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Modeling and Quantifying Parameter Uncertainty of Co‐Seismic Non‐Classical Nonlinearity in Rocks

Niu,

Gabriel,

Seelinger

et al. 2024

JGR Solid Earth

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Dynamic perturbations reveal unconventional nonlinear behavior in rocks, as evidenced by field and laboratory studies. During the passage of seismic waves, rocks exhibit a decrease in elastic moduli, slowly recovering after. Yet, comprehensive physical models describing these moduli alterations remain sparse and insufficiently validated against observations. Here, we demonstrate the applicability of two physical damage models—the internal variable model (IVM) and the continuum damage model (CDM)—to provide quantitative descriptions of nonlinear co‐seismic elastic wave propagation observations. While the IVM uses one internal variable to describe the evolution of elastic material moduli, the CDM damage variable is a mathematical representation of microscopic defects. We recast the IVM and CDM models as nonlinear hyperbolic partial differential equations and implement 1D and 2D numerical simulations using an arbitrary high‐order discontinuous Galerkin method. We verify the modeling results with co‐propagating acousto‐elastic experimental measurements. Subsequently, we infer the parameters for these nonlinear models from laboratory experiments using probabilistic Bayesian inversion and 2D simulations. By adopting the Adaptive Metropolis Markov chain Monte Carlo method, we quantify the uncertainties of inferred parameters for both physical models, investigating their interplay in 70,000 simulations. We find that the damage variables can trade off with the stress‐strain nonlinearity in discernible ways. We discuss physical interpretations of both damage models and that our CDM quantitatively captures an observed damage increase with perturbation frequency. Our results contribute to a more holistic understanding of co‐seismic damage and post‐seismic recovery after earthquakes bridging the worlds of theoretical analysis and laboratory findings.

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Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Modeling and Quantifying Parameter Uncertainty of Co‐Seismic Non‐Classical Nonlinearity in Rocks

Niu,

Gabriel,

Seelinger

et al. 2024

JGR Solid Earth

Self Cite

View full text Add to dashboard Cite

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Challenges in Developing Great Quasi-Monte Carlo Software

Choi,

Ding,

Hickernell

et al. 2024

Springer Proceedings in Mathematics &Amp; Statistics

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Algorithm 1040: The Sparse Grids Matlab Kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification

Piazzola,

Tamellini

2024

ACM Trans. Math. Softw.

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The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be used for approximating high-dimensional functions and, in particular, for surrogate-model-based uncertainty quantification. It is lightweight, high-level and easy to use, good for quick prototyping and teaching; however, it is equipped with some features that allow its use also in realistic applications. The goal of this paper is to provide an overview of the data structure and of the mathematical aspects forming the basis of the software, as well as comparing the current release of our package to similar available software.

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UM-Bridge: Uncertainty quantification and modeling bridge

Cited by 3 publications

References 10 publications

Modeling and Quantifying Parameter Uncertainty of Co‐Seismic Non‐Classical Nonlinearity in Rocks

Modeling and Quantifying Parameter Uncertainty of Co‐Seismic Non‐Classical Nonlinearity in Rocks

Challenges in Developing Great Quasi-Monte Carlo Software

Algorithm 1040: The Sparse Grids Matlab Kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification

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