Reverse Dependencies of pyDOE

The following projects have a declared dependency on pyDOE:

• amcess — Atomic and Molecular Cluster Energy Surface Sampler
• amcess2024 — Atomic and Molecular Cluster Energy Surface Sampler
• AnyPyTools — Python tools and utilities for working with the AnyBody Modeling System
• autonn — Configurable deep neural networks for neural architecture searchand hyper-parameter tuning; Cloud deployment of DNN models
• autoPyTorch — Auto-PyTorch searches neural architectures using smac
• bayes-optim — A Bayesian Optimization Library
• bet — A toolkit for data-consistent stochastic problems.
• clancyLab-squid — A set of modules to aid in atomistic and molecular simulations.
• deep_macrofin — Deep neural network for solving continuous time economic models
• descmap — Automatic feature selection and volcano curve generation.
• desdeo-emo — The python version reference vector guided evolutionary algorithm.
• dessia-common — Common tools for DessIA software
• doepy — Design of experiments generator with simple CSV input/output options
• dynakit — Machine Learning Toolkit for LS-Dyna Simulations
• fastvpinns — A fast tensor-driven variational physics-informed neural network library for solving PDEs.
• fluidon-doepy — Design of experiments generator with simple CSV input/output options
• glennopt — Multi and single objective optimization tool for cfd/computer simulations.
• glis — GLIS - (GL)obal optimization by (I)nverse distance weighting and (S)urrogate radial basis functions
• gpplus — Python Library for Generalized Gaussian Process Modeling
• history-matching — Package contains helper modules for using history matching for parameter optimzation.
• hypermapper — HyperMapper is a multi-objective black-box optimization tool based on Bayesian Optimization.
• lp2ply — Transform lamination parameters into stacking sequences.
• mipego — Mixed Integer Parallel - Efficient Global Optimization with GPU support
• modestpy — FMI-compliant model identification package
• MoReSQUE — Uncertainty quantification module for mosaik
• MVMO — Python package for heuristic optimization
• nglpy — A wrapper library for exposing the C++ neighborhood graph library (NGL) for computing empty region graphs to python
• npsn — Nuclear Power Surrogate Network
• optimyze — Hyperparamter optimization package
• pflacco — A Python implementation and extension to the R package flacco for computing ELA features.
• pinnde — A library for solving differential equations with PINNs and DeepONets
• prosrs — A tree-based parallel surrogate optimization algorithm for optimizing noisy expensive functions
• pvOps — pvops is a python library for the analysis of field collected operational data for photovoltaic systems.
• pwasopt — PWAS/PWASp - Global and Preference-based Optimization with Mixed Variables using (P)iece(w)ise (A)ffine (S)urrogates
• pycup — An auto-calibration tool for environmental models based on heuristic algorithms and uncertainty estimation theory.
• pymecht — This is PYthon-based repository is for MECHanics of Tissue mechanics. The focus is on flexibility of adding new constitutive models and varying their parameters.
• pyrvea — The python version reference vector guided evolutionary algorithm.
• radd — RADD (Race Against Drift-Diffusion model) is a python package for fitting & simulating cognitive models of reinforcement learning and decision-making
• remin — PINN solver implemented in Pytorch
• restoreio — Reconstruct incomplete oceanographic dataset
• rheia — Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems
• rheia-meca2675 — Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems
• samply — A library for computing samplings in arbitrary dimensions
• sbmlsim — sbmlsim are utilities for simulation of SBML.
• sciai — A fundamental framework for simulations of engineering physics
• swiftemulator — Gaussian process emulator for creating synthetic model data across high dimensional parameter spaces, initially developed for use with the SWIFT simulation code.
• SwolfPy — Solid Waste Optimization Life-cycle Framework in Python(SwolfPy).
• tf-pde — Deep learning library for solving partial differential equations
• tremulator — A package to emulate expensive functions using Gaussian processes.
• worstcase — Worst case analysis and sensitivity studies. Extreme Value, Root-Sum-Square, Monte Carlo.