144 citations · 361 across the 19 of their papers we have counts for
19 papers
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
Aleksandra Pachalieva, Daniel O'Malley, Dylan Robert Harp +1
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
Sampling on NISQ Devices: "Who's the Fairest One of All?"
Elijah Pelofske, John Golden, Andreas Bärtschi +2
Modern NISQ devices are subject to a variety of biases and sources of noise that degrade the solution quality of computations carried out on these devices. A natural question that…
Quantum Annealing Algorithms for Boolean Tensor Networks
Elijah Pelofske, Georg Hahn, Daniel O'Malley +2
Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality solutions of NP-hard problems. In this contribution, we explore th…
Threshold-Based Quantum Optimization
John Golden, Andreas Bärtschi, Daniel O'Malley +1
We propose and study Th-QAOA (pronounced Threshold QAOA), a variation of the Quantum Alternating Operator Ansatz (QAOA) that replaces the standard phase separator operator, which e…
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg +4
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inve…