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Found 5 papers, 0 papers with code
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).
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise.
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.
Physics-informed CoKriging model of a redox flow battery
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance.
Learning Unknown Physics of non-Newtonian Fluids
Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
