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Article
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...
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Article
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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Article
Open AccessDevelopment of the Senseiver for efficient field reconstruction from sparse observations
The reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly non...
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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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Article
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 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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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 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 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 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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Chapter and Conference Paper
Boolean Hierarchical Tucker Networks on Quantum Annealers
Quantum annealing is an emerging technology with the potential to solve some of the computational challenges that remain unresolved as we approach an era beyond Moore’s Law. In this work, we investigate the ca...
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Article
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Here we employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equat...
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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
Pre- and post-processing in quantum-computational hydrologic inverse analysis
It was recently shown that certain subsurface hydrological inverse problems—here framed as determining the composition of an aquifer from pressure readings—can be solved on a quantum annealer. However, the qua...
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Article
Theory and Applications of Macroscale Models in Porous Media
Systems dominated by heterogeneity over a multiplicity of scales, like porous media, still challenge our modeling efforts. The presence of disparate length- and time-scales that control dynamical processes in ...
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Article
Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics
We propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transpor...
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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 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
Agent-Based Socio-Hydrological Hybrid Modeling for Water Resource Management
Hybrid socio-hydrological modeling has become indispensable for managing water resources in an increasingly unstable ecology caused by human activity. Most work on the subject has been focused on either qualit...
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Article
Random Renormalization Group Operators Applied to Stochastic Dynamics
Let X(t) be a fixed point the renormalization group operator (RGO), R p,r X(t)=X(rt)/r p . Sc...
