Turbulence Closure with Small, Local Neural Networks: Forced Two-dimensional and β-plane Flows
Srinivasan, K., M. Chekroun, and J. C. McWilliams, 2024. Turbulence Closure with Small, Local Neural Networks: Forced Two-dimensional and β-plane Flows. Journal of Advances in Modeling Earth Systems 16, e2023MS003 795, doi:10.1029/2023MS003795
The High-Frequency and Rare Events Barriers to Neural Closures of Atmospheric Dynamics
Chekroun, M. D., H. Liu, K. Srinivasan, and J. C. McWilliams, 2024. The high-frequency and rare events barriers to neural closures of atmospheric dynamics. Journal of Physics: Complexity 5(2):025 004. doi:10.1088/2632-072X/ad3e59
Theoretical Tools for Understanding the Climate Crisis from Hasselmann’s Programme and Beyond
Lucarini, V., and M. D. Chekroun, 2023. Theoretical Tools for Understanding the Climate Crisis from Hasselmann’s Programme and Beyond. Nature Reviews Physics 5:744–765. doi:10.1038/s42254-023-00650-8.
Optimal Parameterizing Manifolds for Anticipating Tipping Points and Higher-Order Critical Transitions
Chekroun, M.D., H. Liu, and J.C. McWilliams, 2023. Optimal Parameterizing Manifolds for Anticipating Tipping Points and Higher-Order Critical Transitions. Chaos 33(9), 093126. doi:10.1063/5.0167419
Transitions in Stochastic Non-Equilibrium Systems: Efficient Reduction and Analysis
Chekroun, M.D., H. Liu, J.C. McWilliams, and S. Wang, 2023. Transitions in Stochastic Non-Equilibrium Systems: Efficient Reduction and Analysis. Journal of Differential Equations 346, 145–204. doi:10.1016/j.jde.2022.11.025
Deep Reinforcement Learning for Adaptive Mesh Refinement
Foucart, C., A. Charous, and P.F.J. Lermusiaux, 2023. Deep Reinforcement Learning for Adaptive Mesh Refinement. Journal of Computational Physics 491, 112381. doi:10.1016/j.jcp.2023.112381
Bayesian Learning of Coupled Biogeochemical-Physical Models
Gupta, A. and P.F.J. Lermusiaux, 2023. Bayesian Learning of Coupled Biogeochemical-Physical Models. Progress in Oceanography 216, 103050. doi:10.1016/j.pocean.2023.103050
Generalized Neural Closure Models with Interpretability
Gupta, A., and P.F.J. Lermusiaux, 2023. Generalized Neural Closure Models with Interpretability. Scientific Reports 13, 10364. doi:10.1038/s41598-023-35319-w
Confidently Comparing Estimates with the c-value
Trippe, B.L., S.K. Deshpande, and T. Broderick, 2023. Confidently Comparing Estimates with the c-value. Journal of the American Statistical Association 0(0), 1–12. doi:10.1080/01621459.2022.2153688
Generic Generation of Noise-Driven Chaos in Stochastic Time Delay Systems: Bridging the Gap with High-End Simulations
Chekroun, M.D., I. Koren, H. Liu, and H. Liu, 2022. Generic Generation of Noise-Driven Chaos in Stochastic Time Delay Systems: Bridging the Gap with High-End Simulations. Science Advances 8(46), eabq7137. doi:10.1126/sciadv.abq7137
Stochastic Rectification of Fast Oscillations on Slow Manifold Closures
Chekroun, M.D., H. Liu, and J.C. McWilliams, 2021. Stochastic Rectification of Fast Oscillations on Slow Manifold Closures. Proceedings of the National Academy of Sciences of the United States of America 118(48), e2113650118. doi:10.1073/pnas.2113650118
Neural Closure Models for Dynamical Systems
Gupta, A. and P.F.J. Lermusiaux, 2021. Neural Closure Models for Dynamical Systems. Proceedings of The Royal Society A, 477(2252), 1–29. doi:10.1098/rspa.2020.1004
Reduced-order Models for Coupled Dynamical Systems: Data-driven Methods and the Koopman Operator
Santos Gutiérrez, M., V. Lucarini, M.D. Chekroun, and M. Ghil, 2021. Reduced-order Models for Coupled Dynamical Systems: Data-driven Methods and the Koopman Operator. Chaos 31(5), 053116. doi:10.1063/5.0039496
