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Machine learning for climate physics and simulations
We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …
learning (ML) and climate physics, highlighting the use of ML techniques, including …
Kan: Kolmogorov-arnold networks
Z Liu, Y Wang, S Vaidya, F Ruehle, J Halverson… - ar** between points in the spatio-temporal domain and solutions of partial differential …
[PDF][PDF] Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems
Multiscale problems are challenging for neural network-based discretizations of differential
equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed …
equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed …
Multifidelity kolmogorov-arnold networks
We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-
fidelity model along with a small amount of high-fidelity data to train a model for the high …
fidelity model along with a small amount of high-fidelity data to train a model for the high …