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Found 5 papers, 5 papers with code
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Transformers for Modeling Physical Systems
Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text.
Multi-fidelity Generative Deep Learning Turbulent Flows
The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation.
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
In recent years, deep learning has proven to be a viable methodology for surrogate modeling and uncertainty quantification for a vast number of physical systems.
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Uncertainty quantification for such data-driven models is essential since their predictive capability rapidly declines as they are tested for flow physics that deviate from that in the training data.
