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09/19/2023
Multi-fidelity climate model parameterization for better generalization and extrapolation
Machine-learning-based parameterizations (i.e. representation of sub-gri...
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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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02/14/2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Several fundamental problems in science and engineering consist of globa...
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10/26/2022
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
Physical parameterizations are used as representations of unresolved sub...
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10/25/2021
Fast PDE-constrained optimization via self-supervised operator learning
Design and optimal control problems are among the fundamental, ubiquitou...
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03/04/2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
This paper presents a machine learning framework (GP-NODE) for Bayesian ...
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12/15/2020
Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations
We present a simulation-based classification approach for large deployed...
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04/15/2020
Bayesian differential programming for robust systems identification under uncertainty
This paper presents a machine learning framework for Bayesian systems id...
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02/25/2020