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Found 3 papers, 1 papers with code
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations.
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets.
Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs
Training deep neural networks consumes increasing computational resource shares in many compute centers.
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