FootNet is a physics-guided deep learning model that can emulate atmospheric transport. It is trained on half a million examples generated using a physics-based model (STILT). It is 1000x faster than STILT and can compute footprints on-the-fly. 
Here we developed a computationally efficient method for scalable GHG flux inversions using a deep learning emulator of atmospheric transport (FootNet).
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in Chemosphere, 2021
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: "Shukla, K., Dadheech, N., Kumar, P., & Khare, M. (2021). Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi. Chemosphere, 272, 129611."
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Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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