Search Results for author:
Found 6 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.
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators
Constrained optimization of the parameters of a simulator plays a crucial role in a design process.
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.
Model Order Reduction based on Runge-Kutta Neural Network
The first step is dimension reduction by projection-based method, and the second is the model reconstruction by Neural Network.
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics
Corresponding predictions of optimal meshes can be used as input for any mesh generation and CFD tool.
