Robust decision analysis for environmental management of groundwater contamination sites

In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decision-support framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.Comment: This paper has been withdrawn by the author due to a crucial sign error in equations 7 and

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Thesis (Ed.M.)--Boston Universit

Nonnegative/binary matrix factorization with a D-Wave quantum annealer

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images

A Thomistic Argument against the Simulation Hypothesis: An Application of the Doctrine of Sign in John Poinsot

In this paper we will explore how the action of signs underlying all human experience precludes the possibility that we are being systematically deceived in our perception of reality. The simulation hypothesis, as well as similarly motivated skeptical scenarios, such as the brain-in-a-vat hypothesis and Descartes’ evil demon thought experiment, wrongly presuppose a modern, dualistic theory of knowledge, as well as a neuroreductionist model of sensation. However, we will see how the action of signs in human cognition presupposes the existence of a relational mode of being, namely, esse intentionale (“intentional being”), which is immaterial and incapable of subjection to technological manipulation. Furthermore, sensation, the origin of all human knowledge, and ens primum cognitum (“being as first known”), the condition of all human knowledge, both defy materialistic explanations. The doctrine of signs, as masterfully articulated by John Poinsot (John of St. Thomas), recognizes the triadic nature of relations underlying the full range of human experience. A proper understanding of the relationship between mind and world, as well as a recognition of the mistaken presuppositions underlying much of modern philosophy, will help to disillusion those who are convinced by the simulation hypothesis and other similarly motivated skeptical scenarios

Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.Comment: 13 pages, 9 figures; v2 minor revisio