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Found 5 papers, 1 papers with code
Transfer learning driven design optimization for inertial confinement fusion
Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework.
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data
In the context of ICF design, neural network models trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone.
Transfer learning to model inertial confinement fusion experiments
We introduce the idea of hierarchical transfer learning, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data.
Predicting the time-evolution of multi-physics systems with sequence-to-sequence models
In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations.
Deep neural network initialization with decision trees
By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex datasets.
