Certified Adversarial Robustness for Deep Reinforcement Learning. - Björn Lütjens; Michael Everett; Jonathan P. How (CoRL, 2019)

Safe Reinforcement Learning With Model Uncertainty Estimates. - Björn Lütjens; Michael Everett; Jonathan P. How (ICRA, 2019)

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts. - Björn Lütjens; Lucas Liebenwein; Katharina Kramer (CoRR, 2019)

TrueBranch: Metric Learning-based Verification of Forest Conservation Projects. - Simona Santamaria; David Dao; Björn Lütjens; Ce Zhang 001 (CoRR, 2020)

Physics-informed GANs for Coastal Flood Visualization. - Björn Lütjens; Brandon Leshchinskiy; Christian Requena-Mesa; Farrukh Chishtie; Natalia Díaz Rodríguez; Océane Boulais; Aaron Piña; Dava J. Newman; Alexander Lavin; Yarin Gal; Chedy Raïssi (CoRR, 2020)

Graph Neural Networks for Improved El Niño Forecasting. - Salva Rühling Cachay; Emma Erickson; Arthur Fender C. Bucker; Ernest Pokropek; Willa Potosnak; Salomey Osei; Björn Lütjens (CoRR, 2020)

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization. - Björn Lütjens; Brandon Leshchinskiy; Christian Requena-Mesa; Farrukh Chishtie; Natalia Díaz Rodríguez; Océane Boulais; Aruna Sankaranarayanan; Aaron Piña; Yarin Gal; Chedy Raïssi; Alexander Lavin; Dava J. Newman (CoRR, 2021)

The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks. - Salva Rühling Cachay; Emma Erickson; Arthur Fender C. Bucker; Ernest Pokropek; Willa Potosnak; Suyash Bire; Salomey Osei; Björn Lütjens (CoRR, 2021)

PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling. - Björn Lütjens; Catherine H. Crawford; Mark Veillette; Dava J. Newman (CoRR, 2021)

Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery. - Gyri Reiersen; David Dao; Björn Lütjens; Konstantin Klemmer; Xiaoxiang Zhu 001; Ce Zhang 001 (CoRR, 2021)

WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data. - Rupa Kurinchi-Vendhan; Björn Lütjens; Ritwik Gupta; Lucien Werner; Dava J. Newman (CoRR, 2021)

Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark. - Alexandre Lacoste; Evan David Sherwin; Hannah Kerner; Hamed Alemohammad; Björn Lütjens; Jeremy Irvin; David Dao; Alex Chang; Mehmet Gunturkun; Alexandre Drouin; Pau Rodríguez; David Vázquez 001 (CoRR, 2021)

ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. - Gyri Reiersen; David Dao; Björn Lütjens; Konstantin Klemmer; Kenza Amara; Attila Steinegger; Ce Zhang 001; Xiaoxiang Zhu 001 (AAAI, 2022)

Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers. - Björn Lütjens; Catherine H. Crawford; Campbell D. Watson; Christopher Hill; Dava J. Newman (CoRR, 2022)

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning. - Michael Everett; Björn Lütjens; Jonathan P. How (IEEE Trans. Neural Networks Learn. Syst., 2022)

GEO-Bench: Toward Foundation Models for Earth Monitoring. - Alexandre Lacoste; Nils Lehmann; Pau Rodríguez; Evan D. Sherwin; Hannah Kerner; Björn Lütjens; Jeremy Irvin; David Dao; Hamed Alemohammad; Alexandre Drouin; Mehmet Gunturkun; Gabriel Huang; et al. (NeurIPS, 2023)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

Teaching Computer Vision for Ecology. - Elijah Cole; Suzanne Stathatos; Björn Lütjens; Tarun Sharma; Justin Kay; Jason R. Parham; Benjamin Kellenberger; Sara Beery (CoRR, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)

The impact of internal variability on benchmarking deep learning climate emulators. - Björn Lütjens; Raffaele Ferrari; Duncan Watson-Parris; Noelle Selin (CoRR, 2024)

Generating Physically-Consistent Satellite Imagery for Climate Visualizations. - Björn Lütjens; Brandon Leshchinskiy; Océane Boulais; Farrukh Chishtie; Natalia Díaz Rodríguez; Margaux Masson-Forsythe; Ana Mata-Payerro; Christian Requena-Mesa; Aruna Sankaranarayanan; Aaron Piña; Yarin Gal; Chedy Raïssi; et al. (IEEE Trans. Geosci. Remote. Sens., 2024)