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2. AI-Machine Learning-Enabled Tokamak Digital Twin

Author

Tang, William, Feibush, Eliot, Dong, Ge, Borthwick, Noah, Lee, Apollo, Gomez, Juan-Felipe, Gibbs, Tom, Stone, John, Messmer, Peter, Wells, Jack, Wei, Xishuo, and Lin, Zhihong

Subjects
Physics - Computational Physics, Physics - Plasma Physics
Abstract

In addressing the Department of Energy's April, 2022 announcement of a Bold Decadal Vision for delivering a Fusion Pilot Plant by 2035, associated software tools need to be developed for the integration of real world engineering and supply chain data with advanced science models that are accelerated with Machine Learning. An associated research and development effort has been introduced here with promising early progress on the delivery of a realistic Digital Twin Tokamak that has benefited from accelerated advances by the Princeton University AI Deep Learning innovative near-real-time simulators accompanied by technological capabilities from the NVIDIA Omniverse, an open computing platform for building and operating applications that connect with leading scientific computing visualization software. Working with the CAD files for the GA/DIII-D tokamak including equilibrium evolution as an exemplar tokamak application using Omniverse, the Princeton-NVIDIA collaboration has integrated modern AI/HPC-enabled near-real-time kinetic dynamics to connect and accelerate state-of-the-art, synthetic, HPC simulators to model fusion devices and control systems. The overarching goal is to deliver an interactive scientific digital twin of an advanced MFE tokamak that enables near-real-time simulation workflows built with Omniverse to eventually help open doors to new capabilities for generating clean power for a better future.

Published
2024

9. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

Author

Dommer, Abigail, Casalino, Lorenzo, Kearns, Fiona, Rosenfeld, Mia, Wauer, Nicholas, Ahn, Surl-Hee, Russo, John, Oliveira, Sofia, Morris, Clare, Bogetti, Anthony, Trifan, Anda, Brace, Alexander, Sztain, Terra, Clyde, Austin, Ma, Heng, Chennubhotla, Chakra, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Tamayo-Mendoza, Teresa, Welborn, Matthew, Christensen, Anders, Smith, Daniel Ga, Qiao, Zhuoran, Sirumalla, Sai K, O'Connor, Michael, Manby, Frederick, Anandkumar, Anima, Hardy, David, Phillips, James, Stern, Abraham, Romero, Josh, Clark, David, Dorrell, Mitchell, Maiden, Tom, Huang, Lei, McCalpin, John, Woods, Christopher, Gray, Alan, Williams, Matt, Barker, Bryan, Rajapaksha, Harinda, Pitts, Richard, Gibbs, Tom, Stone, John, Zuckerman, Daniel M, Mulholland, Adrian J, Miller, Thomas, Jha, Shantenu, Ramanathan, Arvind, Chong, Lillian, and Amaro, Rommie E

Subjects
AI, COVID-19, Delta, GPU, HPC, SARS-CoV-2, aerosols, computational virology, deep learning, molecular dynamics, multiscale simulation, weighted ensemble, Biodefense, Emerging Infectious Diseases, Pneumonia & Influenza, Infectious Diseases, Prevention, Vaccine Related, Lung, Infection, Good Health and Well Being, Distributed Computing
Abstract

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Published
2023

13. IgG4-Related Disease

Author

Kawano, Mitsuhiro, Zen, Yoh, Saeki, Takako, Dong, Lingli, Zhang, Wen, Della-Torre, Emanuel, Hart, Philip A., Ferry, Judith A., Stone, John H., and Stone, John H., editor

Published
2023
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17. Rheumatoid Vasculitis

Author

Makol, Ashima, Kermani, Tanaz, Warrington, Kenneth, Stone, John H., Matteson, Eric L., and Stone, John H., editor

Published
2023
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19. Giant Cell Arteritis and Polymyalgia Rheumatica

Author

Villiger, Peter M., Christ, Lisa, Seitz, Luca, Scholz, Godehard, Tappeiner, Christoph, Muratore, Francesco, Salvarani, Carlo, Mollan, Sue, Quick, Vanessa, Dejaco, Christian, Lee, Michael, Basu, Neil, Miller, Neil, Stone, John H., and Stone, John H., editor

Published
2023
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21. Cryoglobulinemia

Author

Dammacco, Franco, Cacoub, Patrice, Stone, John H., Saadoun, David, and Stone, John H., editor

Published
2023
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27. Sex as a predictor of clinical phenotype and determinant of immune response in IgG4-related disease: a retrospective study of patients fulfilling the American College of Rheumatology–European League Against Rheumatism classification criteria

Author

Jha, Isha, McMahon, Grace A, Perugino, Cory A, Katz, Guy, Wallace, Zachary S, Fernandes, Ana, Jiang, Bohang, Zhang, Yuqing, McMahon, Aubree E, Guy, Thomas V, Liu, Hang, Hernandez-Barco, Yasmin G, Pillai, Shiv, and Stone, John H

Published
2024
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33. Granzyme K– and amphiregulin-expressing cytotoxic T cells and activated extrafollicular B cells are potential drivers of IgG4-related disease

Author

Koga, Risako, Maehara, Takashi, Aoyagi, Ryuichi, Munemura, Ryusuke, Murakami, Yuka, Doi, Atsushi, Kono, Michihito, Yamamoto, Hidetaka, Niiro, Hiroaki, Kiyoshima, Tamotsu, Tanabe, Mika, Nakano, Toshiaki, Matsukuma, Yuta, Kawano, Mitsuhiro, Stone, John H., Pillai, Shiv, Nakamura, Seiji, and Kawano, Shintaro

Published
2024
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34. A phase 3 randomized, double-blind, placebo-controlled study to evaluate the efficacy and safety of sarilumab in patients with giant cell arteritis

Author

Schmidt, Wolfgang A., Dasgupta, Bhaskar, Sloane, Jennifer, Giannelou, Angeliki, Xu, Yuqing, Unizony, Sebastian H., Mackie, Sarah L., Gonzalez-Gay, Miguel A., Spiera, Robert, Warrington, Kenneth J., Villiger, Peter M., Nivens, Michael C., Akinlade, Bolanle, Lin, Yong, Buttgereit, Frank, and Stone, John H.

Published
2023
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36. Integrated Science of Global Epidemics 2050

Author

Rezaei, Nima, Saghazadeh, Amene, Jraifi, Abdelilah, Siani, Alessandro, Arredondo, Ana Maria Perez, Onoja, Anyebe Bernard, Hamada, Atsuo, Darouichi, Aziz, Son, Barbara W. K., Casais, Beatriz, Zhou, Bingjie, Paital, Biswaranjan, Cremonini, Camilla, Lucatero, Carlos Rodríguez, Liew, Christina, Bales, Connie W., Tartaglia, Dario, Schmiege, Dennis, Munguía-Izquierdo, Diego, Anugwom, Edlyne E., Naumova, Elena N., Cicuttin, Enrico, Coccolini, Federico, Samanta, G. P., Young, Gerald, Martínez-Pérez, Guillermo Z., Kadan, Gül, Kipruto, Hillary, Elmouki, Ilias, Asamani, James Avoka, Bueno-Antequera, Javier, Felappi, Jéssica Francine, Proença, João F., Stone, John, Gottlieb, Jordyn, Ntajal, Joshua, Paris, Juliana Minetto Gellert, Nabyonga-Orem, Juliet, Das, Kabita, Starr, Kathryn N. Porter, Anugwom, Kenechukwu N., Njabo, Kevin Y., Patel, Krupali, Zhong, Ling, Pattanaik, Manaswini, Miller, Marshall G., Chiarugi, Massimo, Hartwick, Meghan A., Savi, Merveille Koissi, Yoshikawa, Minako Jen, Sharif, Nadim, Aral, Neriman, Rahyani, Ni Komang Yuni, Mitchell, Nia S., Simpson, Ryan B., Yasobant, Sandul, Saha, Sangeeta, Cuschieri, Sarah, Liang, Shiwei, Dey, Shuvra Kanti, Grech, Stephan, Sinha, Tapen, Falkenberg, Timo, Wiwanitkit, Viroj, Jiang, Xu, and Rezaei, Nima, Editor-in-Chief

Published
2023
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42. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

Author

Casalino, Lorenzo, Dommer, Abigail C, Gaieb, Zied, Barros, Emilia P, Sztain, Terra, Ahn, Surl-Hee, Trifan, Anda, Brace, Alexander, Bogetti, Anthony T, Clyde, Austin, Ma, Heng, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Chong, Lillian T, Simmerling, Carlos, Hardy, David J, Maia, Julio DC, Phillips, James C, Kurth, Thorsten, Stern, Abraham C, Huang, Lei, McCalpin, John D, Tatineni, Mahidhar, Gibbs, Tom, Stone, John E, Jha, Shantenu, Ramanathan, Arvind, and Amaro, Rommie E

Subjects
Vaccine Related, Biodefense, Infectious Diseases, Prevention, Lung, Emerging Infectious Diseases, Infection, Good Health and Well Being, Molecular dynamics, deep learning, multiscale simulation, weighted ensemble, computational virology, SARS-CoV-2, COVID19, HPC, GPU, AI, Distributed Computing
Abstract

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Published
2021

43. The Future of COVID Cities

Author

Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, Taylor, Elizabeth, Burton, Paul, Series Editor, Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, and Taylor, Elizabeth

Published
2022
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44. Moving Around the COVID City

Author

Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, Taylor, Elizabeth, Burton, Paul, Series Editor, Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, and Taylor, Elizabeth

Published
2022
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45. Housing and the Pandemic

Author

Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, Taylor, Elizabeth, Burton, Paul, Series Editor, Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, and Taylor, Elizabeth

Published
2022
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46. Planning the COVID City

Author

Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, Taylor, Elizabeth, Burton, Paul, Series Editor, Baum, Scott, Baker, Emma, Davies, Amanda, Stone, John, and Taylor, Elizabeth

Published
2022
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