kEnergy ⚡️

Publications

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network. NeurIPS Workshop: Tackling Climate Change with Machine Learning. Kurihana, T., Yeo, K., Szwarcman, D., Elmegreen, B., Mukkavilli, K., Schmude, J., & Klein, L. (2023). arXiv preprint arXiv:2312.13212.

Site-scale methane plume simulation and validation from oil and gas facilities through advanced dispersion, atmospheric modeling, and scientific machine learning. AGU Fall Meeting Abstracts (Vol. 2024, No. 1662, pp. A41G-1662). Fathi, A., Sousa Almeida, J. L., Bentivegna, E., Cardoso, F., Elmegreen, B., Klein, L., Mukkavilli, S. K., Seastream, G., Sundaram, A. & Trojak, W. (2024).

Enhancing wind downscaling with foundation models. AGU Fall Meeting Abstracts (Vol. 2024, p. IN51B-03). Salles Civitarese, D., Guevara Diaz, J. L. L., Mukkavilli, S. K., Schmude, J., Brunet, D., Corbeil, S., Rodney, M., Lehr, P., & Surcel, M. (2024).

Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96. Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, S., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, L., Maharaj, T., Sherwin, E.D., Mukkavilli, S.K., Kording, K.P., Gomes, C., Ng, A.Y., Hassabis, D., Platt, J.C., Creutzig, F., Chayes, J., Bengio., Y. (2022). [1671 citations] (Public Press: MIT Technology Review, National Geographic)

High-Resolution WRF Wind Field Dataset for GHG Applications: Navigating Computational Challenges in Energy Research. AGU Fall Meeting Abstracts (Vol. 2023, No. 8, pp. A12H-08). Mukkavilli, S. K., Trojak, W., Sousa Almeida, J. L., Elmegreen, B., Watson, C., Klein, L., & Brunschwiler, T. (2023).

Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. NY Times Bestseller. P Hawken, C Frischmann, K Wilkinson, R Allard, K Bayuk, J.P. Gouveia, M Mehra, E Toensmeier, C Leahy, C Chissell, O Ashmoore, Z Accuardi, R.U. Ahmed, C Alkire, R Allard, R Becque, E Boeing, J Cabiness, J Chamberlin, D Chen, L Covis, P deSouza, A Goldstein, A Graves, K Gupta, Z Han, Z Hansfather, Y Herbert, A Hong, A Horowitz, T Hottle, D Jaber, D Jagu, D Kane, B.X. Li, S Malaviya, U Malvadkar, A Mason, M Mathur, V Maxwell, D Mead, R Metzel, A Michalko, I Midzic, S.K. Mukkavilli, K Narula, D Papaioannou, M Pedraza, C Petrenko, N Rajvanshi, George R, A Rubinson, A Salazar, A Satre-Meloy, C Shearer, D Siap, K Siman, L Tahkamo, M Valencia, E.V. Thomas, A Wade, M Waite, C Wheeler, C.W. Wright, L.E. Yang, D Yin, K Zame, T Steyer. (2017). [1106 citations] (Public Press: NY Times, CBS News)

Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event. Journal of Applied Meteorology and Climatology, 57(3), 493-515. Mukkavilli, S. K., Prasad, A. A., Taylor, R. A., Troccoli, A., & Kay, M. J. (2018).

Towards a more accurate machine learning multi-model ensemble method for direct solar irradiance forecasts. American Meteorological Society Meeting Abstracts (Vol. 95, p. J6.3). Mukkavilli, S. K., Kay, M. J., Prasad, A. A., Taylor, R., & Troccoli, A. (2015).

Investigating Australian dust aerosol spatiotemporal effects on direct normal irradiance forecasts (Doctoral dissertation, UNSW Sydney). Mukkavilli, S. K. (2018).