Leveraging the power of artificial intelligence and mathematics to spur innovation and novel solutions to challenges at the intersection of climate change and national security
Our Contribution
APL engineers and scientists are building novel AI capabilities to address the emerging national security challenges arising from climate change.
Visit APLâs Climate Security page to learn more about how the Lab is bringing its core competencies to bear on this critical challenge area.
Researchers in APLâs Intelligent Systems Center (ISC) are building on the Labâs existing strengths in remote sensing and deep learning to pinpoint the sources of climate change and increase situational awareness in a rapidly changing environment. For instance, APL is characterizing global greenhouse gas emissions from the road transportation sector as part of the coalition. City-level results are now available on the coalition website. APL is also detecting and forecasting Arctic sea ice to enable navigation in a rapidly changing Arctic.
Related Publications
Mukherjee, R., D. M. Rollend, G. A. Christie, A. HadĆŸiÄ, S. Matson, A. Saksena, M. Hughes, âTowards Indirect Top-Down Road Transport Emissions Estimation,â 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1092-1101 (2021).
Rollend, D., K. Foster, T. Kott, R. Mocharla, R. MunÌoz, N. Fendley, C. Ashcraft, F. Willard, M. Hughes. âMachine Learning for Activity-Based Road Transportation Emissions Estimation,â Tackling Climate Change with Machine Learning Workshop at NeurIPS 2022.
Keller, Mary Ruth, Christine Piatko, Mary Versa Clemens-Sewall, Rebecca Eager, Kevin Foster, Christopher Gifford, Derek Rollend, Jennifer Sleeman, âShort-Term Beaufort Sea-Ice Extent, Forecasting with Deep Learning,â submitted to Artificial Intelligence for the Earth Systems (Fall 2022).
Accelerated Earth Systems Models
ISC researchers are developing operational forecasts and tools for scientific exploration by accelerating Earth systems models. Existing physics models require extensive computational resources and can be time consuming to run, limiting the exploration of possible futures and characterization of uncertainty. We are collaborating with Earth systems researchers to develop:
Deep-learning models to accelerate air quality forecasting
Tools to characterize climate tipping points using surrogate models, generative adversarial networks, and neuro-symbolic reasoning
Enhanced sea-ice models built with physics-inspired neural networks
Related Publications
Sleeman, Jennifer A., David Chung, Chace Ashcraft, Anshu Saksena, G. Jay Brett, Marisa Hughes, Anand Gnanadesikan, Yannis Kevrekidis, Marie-Aude Sabine Pradal, Thomas W. N. Haine, Renske Gelderloos, âUsing Deep Learning for Climate Tipping Point Discovery to Understand Atlantic Meridional Overturning Circulation Collapse,â AGU (December 2022).
Halem, Milton, Jan Mandel, Adam Kochanski, Sen Chiao, Zhifeng Yang, Eugenia Kalnay, Jennifer A. Sleeman, Adam Bargteil, James P. Mackinnon, John Edward Dorband, Yaacov Yesha, Safa Motesharrei, Cheng Da, John Sorkin, Andrea Iorga, Samit Shivadekar, âTowards a Dynamic Multiscale Wildfire Digital Twin,â AGU (December 2022).
Hamer, Sophia, Jennifer A. Sleeman, Ivanka Stajner, Milton Halem, Christoph Keller, Raffaele Montuoro, Kai Wang, Jian-Ping Huang, Ho-Chun Huang, David Allured, James M. Wilczak, Irina Djalalova, Jeffrey McQueen, Barry Baker, Vladimir Krasnopolsky, âForecast Aware Model-Driven Deep Learning Bias Correction for Improved Operational Air Quality Forecasting,â AGU (December 2022).
Brett, Genevieve, Larry H. White, Anand Gnanadesikan, Marie-Aude Sabine Pradal, Renske Gelderloos, Thomas W. N. Haine, Yannis Kevrekidis, Jennifer A. Sleeman, âAMOC Dynamics: Can a Box Model Explain a GlobalModel?â AGU (December 2022).
Sleeman, J, D. Chung, A. Gnanadesikan, J. Brett, Y. Kevrekidis, M. Hughes, T. Haine, M. A. Pradal, R. Gelderloos, C. A. Ashcraft, âGenerative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN),â arXiv preprint arXiv:2302.10274 (2023).
Sleeman, J; D. Chung, C. Ashcraft, J. Brett, A. Gnanadesikan, Y. Kevrekidis, M. Hughes, T. Haine, M. A. Pradal, R. Gelderloos, C. Tang, A. Saxsena, L. White, âUsing Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points.â arXiv preprint arXiv:2302.06852 (2023).
Ashcraft, C; J. Sleeman, J. Brett, A. Gnanadesikan, âA Bidirectional Neuro-symbolic Methodology for Translating Between Generative Latent Representations and Natural Language Questions,â AAAI Spring Symposium (2023).
ISC researchers are developing AI methods to reduce the carbon footprint of various activities while maintaining or improving effectiveness. This includes leveraging reinforcement learning to train more efficient systems for adaptive HVAC control and optimizing crop yield.
Related Publications
Markowitz, J., N. Drenkow, âEfficient HVAC Control with Deep Reinforcement Learning and EnergyPlus,â ICLR 2023 Workshop on Tackling Climate Change with Machine Learning (2023).
Ashcraft, C., K. Karra, âMachine Learning aid Crop Yield Optimization,â Where AI Meets Food Security Workshop at AAAI Fall Symposium Series 2021.
Advanced Systems Modeling and Integration
To combat climate and resource challenges, it is essential to understand and capture the complex interdependencies between different scales and areas of interest. The ISCâs approach to this problem includes using modular modeling frameworks to capture system interdependencies, applying network analysis to green power and food systems, and creating geographic overlays of Arctic infrastructure and anticipated climate changes.
Related Publications
Hughes, M., et al., âSystem Integration with Multiscale Networks (Simon): A Modular Framework for Resource Management Models,â 2020 Winter Simulation Conference (WSC), pp. 656â667, doi:10.1109/WSC48552.2020.9383983 (2020).
Reilly, E. P., S. Agarwala, M. T. Kelbaugh, A. Ciesielski, H.-J. M. Ebeid, M. Hughes, âModeling the Relationship Between Food and Civil Conflict,â 2020 Winter Simulation Conference (WSC), pp. 715â726, doi:10.1109/WSC48552.2020.9384007 (2020).