Error estimates for the Cahn-Hilliard equation with dynamic boundary conditions. - Paula Harder; Balázs Kovács (CoRR, 2020)

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations. - Paula Harder; William K. Jones; Redouane Lguensat; Shahine Bouabid; James Fulton; Dánell Quesada-Chacón; Aris Marcolongo; Sofija Stefanovic; Yuhan Rao; Peter Manshausen; Duncan Watson-Parris (CoRR, 2020)

SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain. - Paula Harder; Franz-Josef Pfreundt; Margret Keuper; Janis Keuper (IJCNN, 2021)

Emulating Aerosol Microphysics with Machine Learning. - Paula Harder; Duncan Watson-Parris; Dominik Strassel; Nicolas R. Gauger; Philip Stier; Janis Keuper (CoRR, 2021)

Detecting AutoAttack Perturbations in the Frequency Domain. - Peter Lorenz; Paula Harder; Dominik Strassel; Margret Keuper; Janis Keuper (CoRR, 2021)

Physics-Informed Learning of Aerosol Microphysics. - Paula Harder; Duncan Watson-Parris; Philip Stier; Dominik Strassel; Nicolas R. Gauger; Janis Keuper (CoRR, 2022)

Abstract error analysis for Cahn-Hilliard type equations with dynamic boundary conditions. - Paula Harder (CoRR, 2022)

Generating physically-consistent high-resolution climate data with hard-constrained neural networks. - Paula Harder; Qidong Yang; Venkatesh Ramesh; Prasanna Sattigeri; Alex Hernández-García; Campbell D. Watson; Daniela Szwarcman; David S. Rolnick (CoRR, 2022)

Identifying the Causes of Pyrocumulonimbus (PyroCb). - Emiliano Díaz Salas-Porras; Kenza Tazi; Ashwin Braude; Daniel Okoh; Kara D. Lamb; Duncan Watson-Parris; Paula Harder; Nis Meinert (CoRR, 2022)

Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds. - Kenza Tazi; Emiliano Díaz Salas-Porras; Ashwin Braude; Daniel Okoh; Kara D. Lamb; Duncan Watson-Parris; Paula Harder; Nis Meinert (CoRR, 2022)

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. - Qidong Yang; Alex Hernández-García; Paula Harder; Venkatesh Ramesh; Prasanna Sattigeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (CoRR, 2023)

Multi-variable Hard Physical Constraints for Climate Model Downscaling. - Jose González-Abad; Alex Hernández-García; Paula Harder; David Rolnick; José Manuel Gutiérrez Llorente (CoRR, 2023)

Hard-Constrained Deep Learning for Climate Downscaling. - Paula Harder; Alex Hernández-García; Venkatesh Ramesh; Qidong Yang; Prasanna Sattegeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (J. Mach. Learn. Res., 2023)

Climate Variable Downscaling with Conditional Normalizing Flows. - Christina Winkler; Paula Harder; David Rolnick (CoRR, 2024)

Evaluating the transferability potential of deep learning models for climate downscaling. - Ayush Prasad; Paula Harder; Qidong Yang; Prasanna Sattegeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (CoRR, 2024)

Superresolution of Lunar Satellite Images for Enhanced Robotic Traverse Planning: Maximizing the Value of Existing Data Products for Space Robotics. - Jose Ignacio Delgado-Centeno; Paula Harder; Valentin Tertius Bickel; Ben Moseley; Freddie Kalaitzis; Siddha Ganju; Miguel A. Olivares-Méndez (IEEE Robotics Autom. Mag., 2024)

RainShift: A Benchmark for Precipitation Downscaling Across Geographies. - Paula Harder; Luca Schmidt; Francis Pelletier; Nicole Ludwig; Matthew Chantry; Christian Lessig; Alex Hernández-García; David Rolnick (CoRR, 2025)