Surrogate modelling technique selectors
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Updated
Apr 25, 2019 - Python
Surrogate modelling technique selectors
Design of a surrogate-assisted (mu/mu,lambda)-ES
Surrogate model library for Derivative-Free Optimization
Learning Aerodynamics Through Data to Improve Optimization Algorithms
Heron is a surrogate modelling toolkit for python, using Gaussian process regression.
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
Design optimization of floating offshore wind turbine with surrogate models
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.
A design optimization study of underwater vehicle using Bayesian optimization and deep learning based surrogate model
A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives
Tutorials for the C++ pressio library
This repository contains scripts that were used for the experiments of our work named "Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling".
This repository contains the packages that build the problem objects for the desdeo framework.
Hierarchical generative and regressive machine learning for next generation materials screening
Standardized Benchmark Dataset for Localized Exposure to a Realistic Source at 10-90 GHz
DL models for generating stress fields in microstructures
Reinforcement Learning Actors for Blade Pitch Control in Wind Turbine Systems
Statistical learning models library for blackbox optimization
Numerical Characterization of Photonic Power Splitter: Optimization Criteria for Forward Modeling
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