NERSC AI Publications
Deep Learning Applications
NERSC is deeply involved in several projects to push the state-of-the-art in deep learning for science. Our engineers, postdocs, and interns collaborate with scientists from a wide range of domains including high energy physics, climate and weather modeling, chemistry/materials, and biosciences. We work with multi-disciplinary teams from LBNL, external institutions, and industry.
Recent publications include:
- Mahesh et al. 2024, "Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators" https://arxiv.org/abs/2408.03100
- Mahesh et al. 2024, "Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators" https://arxiv.org/abs/2408.01581v1
- Pathak et al. 2024, "Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling" https://www.arxiv.org/abs/2408.10958
- Mikuni & Nachmann 2024, "OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks" https://arxiv.org/abs/2404.16091
- Jacobus et al. 2023. "Reconstructing Lyα Fields from Low-resolution Hydrodynamical Simulations with Deep Learning" Published in The Astrophysical Journal 10.3847/1538-4357/acfcb5
- Brenowitz et al., 2024. "A Practical Probabilistic Benchmark for AI Weather Models" https://arxiv.org/abs/2401.15305
- McCabe et al., 2023. "Towards stability of autoregressive neural operators" Published in Transactions on Machine Learning Research. https://arxiv.org/abs/2306.10619
- Kurth et al., 2023. "Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators" PASC Best Paper. doi:10.1145/3592979.3593412
- Chen et al., 2024, "Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning". https://arxiv.org/abs/2402.15734
- Subramanian et al., 2023, "Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior." Accepted to NeurIPS 2023. https://arxiv.org/abs/2306.00258
- Pathak et al., 2022, “FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.” https://arxiv.org/abs/2202.11214
- Stein et al., 2021, “Mining for strong gravitational lenses with self-supervised learning" - submitted to The Astrophysical Journal and under review. https://arxiv.org/abs/2110.00023
- Hayat et al., 2021, “Self-supervised Representation Learning for Astronomical Images” - published in The Astrophysical Journal, vol. 911, doi:10.3847/2041-8213/abf2c7
- Harrington et al., 2021, “Fast, high-fidelity Lyman α forests with convolutional neural networks” - submitted to The Astrophysical Journal and under review. https://arxiv.org/abs/2106.12662
- Horowitz et al. 2021, “HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics” - submitted to The Astrophysical Journal and under review. https://arxiv.org/abs/2106.12675
- Jiang et al., 2020, “MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework” - https://arxiv.org/abs/2005.01463, accepted for publication at SC20 and Best Student Paper Finalist
- Chattopadhyay et al., 2020, “Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence” - https://eartharxiv.org/cqmb2/, accepted for publication at Climate Informatics 2020
- Mayur Mudigonda et al., 2020, “Climatenet: Bringing The Power Of Deep Learning To Weather And Climate Sciences Via Open Datasets And Architectures,” ICLR 2020
- Karthik Kashinath, Mayur Mudigonda, Kevin Yang, Jiayi Chen, Annette Greiner, and Prabhat Prabhat, 2019, “ClimateNet: Bringing the power of Deep Learning to weather and climate sciences via open datasets and architectures,” Proceedings of the 9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN-561+PROC). doi:10.5065/y82j-f154
- Wang et al., 2020, “Towards Physics-informed Deep Learning for Turbulent Flow Prediction,,” KDD, https://doi.org/10.1145/3394486.3403198
- Jiang et al., 2020, “Enforcing Physical Constraints in CNNs through Differentiable PDE Layer,” ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations
- Muszynski et al., 2020, “Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data,” ICPR200
- Wu et al., 2020, "Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems,” JCP, Volume 406, 109209 https://doi.org/10.1016/j.jcp.2019.109209
- Toms et al., 2020, Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation, Geosci. Model Dev
- “ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning” - https://arxiv.org/abs/2006.00719, submitted to triple AAAI
- Prabhat et al., "ClimateNet: an expert-labeled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather,” Geoscientific Model Development.
- Kashinath et al., 2020, “Physics-informed knowledge-guided Machine Learning for weather and climate modeling: progress and challenges”, Proceedings of the Royal Society, https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0093
- Pathak et al., 2020, “Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations,” https://arxiv.org/abs/2010.00072
- Pathak et al., 2020, “ML-PDE: A Framework for a Machine Learning Enhanced PDE Solver,” NeurIPS ML4PS
- Hayat et al. “Estimating Galactic Distances From Images UsingSelf-supervised Representation Learning,” NeurIPS ML4PS