Data Science for Climate

Presentations

• Leveraging Interpretable Machine Learning for Climate Physics

  ORAL · Invited

  March 9, 2023, 6:00 PM – March 9, 2023, 6:36 PM

  Presenters

  • Laure Zanna

    New York University (NYU)

  Authors

  • Laure Zanna

    New York University (NYU)

  • Andrew Ross

    NYU

  • Pavel Perezhogin

    NYU

  • Carlos Fernandez-Granda

    NYU

  • Ziwei Li

    NYU

  View abstract →

• Probabilistic learning for predictive modeling of climate variability

  ORAL

  March 9, 2023, 6:36 PM – March 9, 2023, 6:48 PM

  Publication: 1. Nadiga, B.T., 2021. Reservoir computing as a tool for climate predictability studies. Journal of Advances in Modeling Earth Systems, 13(4), p.e2020MS002290.<br>2. Luo, X., Nadiga, B.T., Ren, Y., Park, J.H., Xu, W. and Yoo, S., 2022. A Bayesian Deep Learning Approach to Near-Term Climate Prediction. arXiv preprint arXiv:2202.11244 (accepted in Journal of Advances in Modeling Earth Systems)<br>

  Presenters

  • Balu Nadiga

    LANL

  Authors

  • Balu Nadiga

    LANL

  View abstract →

• Learning fire spread dynamics with physics-constrained machine learning

  ORAL

  March 9, 2023, 6:48 PM – March 9, 2023, 7:00 PM

  Presenters

  • Jatan Buch

    Columbia University

  Authors

  • Jatan Buch

    Columbia University

  • Aniket Jivani

    University of Michigan Ann Arbor

  • Xun Huan

    University of Michigan Ann Arbor

  • A. Park Williams

    University of California Los Angeles

  • Pierre Gentine

    Columbia University

  View abstract →

• Long-term instability of deep learning-based digital twins of the climate system: Cause and solution

  ORAL

  March 9, 2023, 7:00 PM – March 9, 2023, 7:12 PM

  Publication: Long-term instability of deep learning-based digital twins of the climate system: Cause and solution, in review

  Presenters

  • Ashesh K Chattopadhyay

    Rice University

  Authors

  • Ashesh K Chattopadhyay

    Rice University

  • Pedram Hassanzadeh

    Rice University

  View abstract →

• Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

  ORAL

  March 9, 2023, 7:12 PM – March 9, 2023, 7:24 PM

  Publication: S. Chen, C. Li, Y. Xie, and M. Li, "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," submitted to Applied Energy, 2022.

  Presenters

  • Shanlin Chen

    Hong Kong Polytechnic University

  Authors

  • Shanlin Chen

    Hong Kong Polytechnic University

  View abstract →

• Integrating the spectral analyses of neural networks and climate physics for stable, explainable, and generalizable models

  ORAL

  March 9, 2023, 7:24 PM – March 9, 2023, 7:36 PM

  Publication: Parts of the results have been reported in https://arxiv.org/abs/2206.03198

  Presenters

  • Pedram Hassanzadeh

    Rice University

  Authors

  • Pedram Hassanzadeh

    Rice University

  • Yifei Guan

    Rice University

  • Adam Subel

    Rice Univ

  • Ashesh K Chattopadhyay

    Rice University

  View abstract →

• Reduced-order modeling of Arctic Amplification feedbacks

  ORAL

  March 9, 2023, 7:36 PM – March 9, 2023, 7:48 PM

  Presenters

  • Adam Rupe

    Pacific Northwest National Laboratory

  Authors

  • Adam Rupe

    Pacific Northwest National Laboratory

  • Craig Bakker

    Pacific Northwest National Laboratory

  • Derek DeSantis

    Los Alamos National Laboratory

  • Jian Lu

    Pacific Northwest National Laboratory

  View abstract →

• Physics-informed and Equality-constrained Artificial Neural Networks with Applications to Partial Differential Equations and Multi-fidelity Data Assimilation

  ORAL · Invited

  March 9, 2023, 7:48 PM – March 9, 2023, 8:24 PM

  Publication: Basir, Shamsulhaq, and Inanc Senocak. "Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion." Journal of Computational Physics (2022): 111301.<br><br>Basir, Shamsulhaq. "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)." arXiv preprint arXiv:2209.09988 (2022).

  Presenters

  • shamsulhaq basir

    University of Pittsburgh

  Authors

  • shamsulhaq basir

    University of Pittsburgh

  • Inanc Senocak

    University of Pittsburgh

  View abstract →

• Energy harvesting by an intelligent body from turbulence

  ORAL

  March 9, 2023, 8:24 PM – March 9, 2023, 8:36 PM

  Presenters

  • Yagmur Kati

    Humboldt University of Berlin

  Authors

  • Yagmur Kati

    Humboldt University of Berlin

  • sinan gundogdu

    Humboldt Universitat zu Berlin

  • Bruno Andreis

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea

  • Sabine Klapp

    Institute for Theoretical Physics, Technical University of Berlin, 10623 Berlin, Germany

  View abstract →

• Numerical proof of shell model turbulence closure

  ORAL

  March 9, 2023, 8:36 PM – March 9, 2023, 8:48 PM

  Publication: https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.7.L082401

  Presenters

  • Giulio Ortali

    Eindhoven University of Technology

  Authors

  • Giulio Ortali

    Eindhoven University of Technology

  • Alessandro Corbetta

    Eindhoven University of Technology

  • Gianluigi Rozza

    SISSA - International School for Advanced Studies

  • Federico Toschi

    Eindhoven University of Technology

  View abstract →