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Found 6 papers, 4 papers with code
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method.
A multifidelity approach to continual learning for physical systems
We introduce a novel continual learning method based on multifidelity deep neural networks.
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN).
Latent Space Simulation for Carbon Capture Design Optimization
This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization.
BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images
The tool could be used to provide benchmarking and training data for existing image processing algorithms and to guide the future development of bubble detecting algorithms.
