Python library for parallel multiobjective simulation optimization

Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions

This is the official repository of the AI for TSP competition at IJCAI 2021

🎯📈 Sequential And Model-Based Optimization with SCE-UA, SMBO, and SHGO algos. No deps, record perfomance.

A toolbox for the calibration and evaluation of simulation models.

MITIM (MIT Integrated Modeling) Suite for Fusion Applications

Python platform for parallel Surrogate-Based Optimization

MVRSM algorithm for optimising mixed-variable expensive cost functions.

UQPyL is a python package for uncertainty quantification and parameter optimization. 参数不确定性分析及优化工具包。

A simple proof-of-concept to optimize airfoils with OpenFOAM and differential evolution.

Benchmark suite for active learning (with surrogate models) in scientific discovery, featuring standardised tasks in materials science and biology

Surrogate-based 0-th Order Global Optimization for black-box problems.

surF - a surrogate modeling method based on Discrete Fourier Transform

IDDEFIX is a physics-informed evolutionary optimization framework that fits a resonator-based model (parameterized by R, f, Q) to wakefield simulation data.

General-purpose library for fitting models to data with correlated Gaussian-distributed noise

Python package for design of experiments

A Surrogate-Assisted Evolutionary Algorithm with Hypervolume Triggered Fidelity Adjustment for Noisy Multiobjective Integer Programming

Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.

This repository is part of a Master's degree thesis project in the Chemical Engineering Department of Politecnico di Milano and written by Hikmet Batuhan Oztemel. This project focuses on applying Trust Region Filter (TRF) methods in surrogate-based optimization (SBO) for chemical process optimization in methanol synthesis plant with Aspen Plus.

A transformative approach to manufacturing optimization, focusing on the textile forming process. This research synergizes domain-specific knowledge with simulation modeling and introduces Bayesian optimization for efficient parameter space exploration.