Björn Lütjens

According to our database, Björn Lütjens authored at least 19 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.

CoRR, 2023

Teaching Computer Vision for Ecology.

Elijah Cole, Suzanne Stathatos, Björn Lütjens, Tarun Sharma, Justin Kay, Jason Parham, Benjamin Kellenberger, Sara Beery

CoRR, 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, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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, David Vázquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiaoxiang Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers.

Björn Lütjens, Catherine H. Crawford, Campbell D. Watson, Christopher Hill, Dava Newman

CoRR, 2022

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, Xiaoxiang Zhu

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

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

CoRR, 2021

WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data.

Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta, Lucien Werner, Dava 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, Ce Zhang

CoRR, 2021

PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling.

Björn Lütjens, Catherine H. Crawford, Mark Veillette, Dava 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

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 Newman

CoRR, 2021

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

Physics-informed GANs for Coastal Flood Visualization.

Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi

CoRR, 2020

TrueBranch: Metric Learning-based Verification of Forest Conservation Projects.

Simona Santamaria, David Dao, Björn Lütjens, Ce Zhang

CoRR, 2020

2019

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts.

Björn Lütjens, Lucas Liebenwein, Katharina Kramer

CoRR, 2019

Safe Reinforcement Learning With Model Uncertainty Estimates.

Björn Lütjens, Michael Everett, Jonathan P. How

Proceedings of the International Conference on Robotics and Automation, 2019

Certified Adversarial Robustness for Deep Reinforcement Learning.

Björn Lütjens, Michael Everett, Jonathan P. How

Proceedings of the 3rd Annual Conference on Robot Learning, 2019