132 citations · 181 across the 8 of their papers we have counts for
9 papers
Robust design under uncertainty in quantum error mitigation
Maksym Prodius, Piotr Czarnik, Michael McKerns +2
Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical post-processing of quantum computation outcomes is a popular approach for error mitigati…
Predictive Scale-Bridging Simulations through Active Learning
Satish Karra, Mohamed Mehana, Nicholas Lubbers +12
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-b…
Improving the efficiency of learning-based error mitigation
Piotr Czarnik, Michael McKerns, Andrew T. Sornborger +1
Error mitigation will play an important role in practical applications of near-term noisy quantum computers. Current error mitigation methods typically concentrate on correction qu…
Machine Learning Changes the Rules for Flux Limiters
Nga Nguyen-Fotiadis, Michael McKerns, Andrew Sornborger
Learning to integrate non-linear equations from highly resolved direct numerical simulations (DNSs) has seen recent interest for reducing the computational load for fluid simulatio…
Optimal Bounds on Nonlinear Partial Differential Equations in Model Certification, Validation, and Experimental Design
M. McKerns, F. J. Alexander, K. S. Hickmann +2
We demonstrate that the recently developed Optimal Uncertainty Quantification (OUQ) theory, combined with recent software enabling fast global solutions of constrained non-convex o…
Building a Framework for Predictive Science
Michael M. McKerns, Leif Strand, Tim Sullivan +2
Key questions that scientists and engineers typically want to address can be formulated in terms of predictive science. Questions such as: "How well does my computational model rep…
The Optimal Uncertainty Algorithm in the Mystic Framework
M. McKerns, H. Owhadi, C. Scovel +2
We have recently proposed a rigorous framework for Uncertainty Quantification (UQ) in which UQ objectives and assumption/information set are brought into the forefront, providing a…
Optimal uncertainty quantification for legacy data observations of Lipschitz functions
T. J. Sullivan, M. McKerns, D. Meyer +3
We consider the problem of providing optimal uncertainty quantification (UQ) --- and hence rigorous certification --- for partially-observed functions. We present a UQ framework wi…
Optimal Uncertainty Quantification
Houman Owhadi, Clint Scovel, Timothy John Sullivan +2
We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, whi…