MITIM (MIT Integrated Modeling) Suite for Fusion Applications
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Updated
May 16, 2024 - Python
MITIM (MIT Integrated Modeling) Suite for Fusion Applications
Generalized and Efficient Blackbox Optimization System
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Bayesian Optimization and Design of Experiments
Jointly-trained tree kernels for Gaussian processes
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
An evaluation framework for machine learning models simulating high-throughput materials discovery.
Pre-trained Gaussian processes for Bayesian optimization
Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production.
Experimental design and (multi-objective) bayesian optimization.
Demos of Cheetah being used for various applications presented in "Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations"
User-friendly Graphical User Interface (GUI) developed at the National Institute for Materials Science (NIMS) for performing statistical data analysis, machine learning (ML) modelisation, and composition/process optimisation through active learning assisted by Bayesian optimisation
Boax is a Bayesian Optimization library for JAX.
Computer-aided molecular and process design using Bayesian optimization
Scalable Non-myopic Bayesian Optimization in Dynamic Cost Settings
Spatial analysis and modelling from traffic accidents in New Zealand.
BOSS (Bayesian Optimization with Semiparametric Surrogate)
Optimising chemical reactions using machine learning
Multithreading, Collections, Optimization, Span Text, Runtime Generation and other classes you might find essential.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
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