Structural Abnormality Detection of ARVC Patients via Localised Distance-to-Average Mapping. - Kristin McLeod; Marcus Michael Noack; Jørg Saberniak; Kristina Haugaa (STACOM, 2014)

A two-scale method using a list of active sub-domains for a fully parallelized solution of wave equations. - Marcus Michael Noack (J. Comput. Sci., 2015)

Hybrid genetic deflated Newton method for global optimisation. - Marcus Michael Noack; Simon W. Funke (J. Comput. Appl. Math., 2017)

Computational Strategies to Increase Efficiency of Gaussian-Process-Driven Autonomous Experiments. - Marcus Michael Noack; Petrus H. Zwart (XLOOP@SC, 2019)

K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. - Charles N. Melton; Marcus Michael Noack; Taisuke Ohta; Thomas E. Beechem; Jeremy Robinson; Xiaotian Zhang; Aaron Bostwick; Chris Jozwiak; Roland J. Koch; Petrus H. Zwart; Alexander Hexemer; Eli Rotenberg (Mach. Learn. Sci. Technol., 2020)

High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning. - Marcus Michael Noack; David Perryman; Harinarayan Krishnan; Petrus H. Zwart (XLOOP@SC, 2021)

Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes. - Marcus Michael Noack; James A. Sethian (CoRR, 2021)

Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels. - Marcus Michael Noack; Harinarayan Krishnan; Mark D. Risser; Kristofer G. Reyes (CoRR, 2022)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes. - Marcus Michael Noack; Hengrui Luo; Mark D. Risser (CoRR, 2023)

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data. - Mark D. Risser; Marcus Michael Noack; Hengrui Luo; Ronald Pandolfi (CoRR, 2024)