Related Workshops

AI4CH4 Workshop Series

AI4CH4 Workshop Report

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