A python package for parameter uncertainty quantification and optimization
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Updated
May 14, 2024 - Python
A python package for parameter uncertainty quantification and optimization
Jupyter book for AAE 590 - Surrogate methods
DrivAerNet: A Parametric Car Dataset for Data-driven Aerodynamic Design and Graph-Based Drag Prediction
surrogate quantitative interpretability for deepnets
squid repository for manuscript analysis
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
Numerical Characterization of Photonic Power Splitter: Optimization Criteria for Forward Modeling
Statistical learning models library for blackbox optimization
Reinforcement Learning Actors for Blade Pitch Control in Wind Turbine Systems
DL models for generating stress fields in microstructures
Standardized Benchmark Dataset for Localized Exposure to a Realistic Source at 10-90 GHz
Hierarchical generative and regressive machine learning for next generation materials screening
This repository contains the packages that build the problem objects for the desdeo framework.
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".
Tutorials for the C++ pressio library
A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives
A design optimization study of underwater vehicle using Bayesian optimization and deep learning based surrogate model
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.
Design optimization of floating offshore wind turbine with surrogate models
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