12 Result(s)
within
Daniel O’Malley
Science, Humanities and Social Sciences, multidisciplinary
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Article
Open AccessAn approach to quantum-computational hydrologic inverse analysis
Making predictions about flow and transport in an aquifer requires knowledge of the heterogeneous properties of the aquifer such as permeability. Computational methods for inverse analysis are commonly used to...
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Article
Open AccessQuantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning
Fractured systems are ubiquitous in natural and engineered applications as diverse as hydraulic fracturing, underground nuclear test detection, corrosive damage in materials and brittle failure of metals and c...
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Article
Open AccessA machine learning framework for rapid forecasting and history matching in unconventional reservoirs
We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to r...
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Article
Open AccessQuantum annealing algorithms for Boolean tensor networks
Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and eff...
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Open AccessPhysics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO \(_2\) ...
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Article
Open AccessEnhancing high-fidelity nonlinear solver with reduced order model
We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. ...
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Article
Open AccessReduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (D...
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Article
Open AccessQuantum computing and preconditioners for hydrological linear systems
Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear ...
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Open AccessPhysics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface
Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and ac...
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Article
Open AccessQuantum algorithms for geologic fracture networks
Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglec...
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Open AccessAddressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks
Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come ...
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Open AccessProgressive transfer learning for advancing machine learning-based reduced-order modeling
To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key c...
