Related Workshops


AI4CH4 Workshop Series

AI4CH4 Workshop Report

AI4CH4 Workshop Agenda

AI4CH4 Presentations

Speaker Name Title
Todd Hay Cloud, HPC, Edge for Science and Security (CHESS)
Sara Knox FluxNet-CH4: A global eddy covariance CH4 database to answer regional and global questions related to CH4 cycling
Ben Poulter Remote sensing of atmospheric methane
Youmi Oh Estimation of global methane soil sink using multi-source datasets and knowledge-guided machine learning
William Riley Wetland methane modeling: Mechanisms, modeling, extrapolation, and challenges
Ben Sulman Simulating process-based biogeochemistry in ELM using reactive transport model coupling
Ewa Deelman Pegasus, a workflow management solution for edge to cloud
Genevieve Noyce Using field manipulation experiments to understand methane dynamics
Christopher Henry Building mechanistic understanding of environmental microbiomes

AI4CH4 White Papers

Number Title Author
AI4CH401 Partitioning net wetland CH4 emissions into production and oxidation components using ecosystem scale flux measurements and physically guided machine learning Qing Zhu
AI4CH402 Upscaling global wetland methane emissions with causality guided machine learning Kunxiaojia Yuan
AI4CH403 Cloud and HPC ecosystems for scientific experiments Nathan Tallent
AI4CH404 Accelerated trait-based modeling of biogenic methane dynamics using physics guided machine learning Jinyun Tang
AI4CH405 Integrating genomic and flux data to develop predictive models for managing methane emissions Clifton Bueno de Mesquita
AI4CH406 Uncertainty in global time-resolved methane emissions from aquatic waterbodies Jemma Stachelek
AI4CH407 Estimation of global methane soil sink using multi-source datasets and knowledge-guided machine learning Youmi Oh
AI4CH408 Toward spatiotemporally resolved methane emissions for modeling and upscaling research Housen Chu
AI4CH409 Scaling genes to global methane modeling through artificial intelligence Xiaofeng Xu
AI4CH410 A machine-learning data assimilation method to improve lake methane prediction Zeli Tan
AI4CH411 Physics-guided machine learning of wildfire methane emissions Fa Li
AI4CH412 Characterizing remotely sensed CH4 through biogenic and anthropogenic flux source attribution: an ecosystem embedding approach Dan Krofcheck
AI4CH413 Implementing and benchmarking an agricultural methane emissions model in E3SM Kendalynn Morris
AI4CH414 AI4 plant trait-based wetland CH4 predictions Avni Malhotra
AI4CH415 Expanding eddy covariance measurements from tropical wetland methane emissions to improve AI-aided emissions upscaling Kyle Delwiche
AI4CH416 A hybrid approach to improve Earth system model predicitions of CH4 emissions from northern peatlands Dan Ricciuto
AI4CH417 The potential for artificial intelligence to inform pore-scale patterns of methane production, release, and consumption using imaging, real-time flux measurements, and microbial modeling Melanie Mayes
AI4CH418 Elucidating environmental regulation on microbial-mediated soil methane emissions using gene-to-ecoystem level data and artificial intelligence Yang Song
AI4CH419 AI for advanced sensor data collection, automation, and processing for the methane cycle Maruti Mudunuru
AI4CH420 Improving predictability of methane emissions from terrestrial ecosystems and terrestrial-aquatic interfaces through machine learning approaches Debjani Sihi
AI4CH421 Merging top-down and bottom-up estimated wetland CH4 emissions using AI/ML Sha Feng
AI4CH422 Coupling AI-based modeling and molecular soil organic matter at regional-scale Satish Karra