The Adaptive Particle Representation (APR) for Simple and Efficient Adaptive Resolution Processing, Storage and Simulations. - Bevan L. Cheeseman (2018)

Stability selection enables robust learning of partial differential equations from limited noisy data. - Suryanarayana Maddu; Bevan L. Cheeseman; Ivo F. Sbalzarini; Christian L. Müller (CoRR, 2019)

Learning physically consistent mathematical models from data using group sparsity. - Suryanarayana Maddu; Bevan L. Cheeseman; Christian L. Müller; Ivo F. Sbalzarini (CoRR, 2020)

STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations. - Suryanarayana Maddu; Dominik Sturm; Bevan L. Cheeseman; Christian L. Müller; Ivo F. Sbalzarini (CoRR, 2021)

Parallel Discrete Convolutions on Adaptive Particle Representations of Images. - Joel Jonsson; Bevan L. Cheeseman; Suryanarayana Maddu; Krzysztof Gonciarz; Ivo F. Sbalzarini (IEEE Trans. Image Process., 2022)

APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images. - Joel Jonsson; Bevan Leslie Cheeseman; Ivo F. Sbalzarini (Trans. Mach. Learn. Res., 2025)