AntonioE89

OMLT

Represent trained machine learning models as Pyomo optimization formulations

TS-EMO

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

UKF-SNMPC

This repository contains the source code for “Unscented Kalman filter stochastic nonlinear model predictive control” (UKF-SNMPC).

Aspen_HYSYS_Python

Aspen HYSYS - Python connection

distributionally_robust_optimization

pc-gym

Reinforcement learning environments for process control applications.

Data-driven-distributionally-robust-MPC

noise-tolerant-bfgs

EvalRetro

A repository for evaluating single-step retrosynthesis algorithms

pulsed-reactor-optimisation

This repository contains code to optimise the geometric parameters and operating conditions of a computational fluid dynamics model of a pulsed flow helical tube reactor.

reactor_benchmark

A set of reactor design benchmark problems to evaluate high-dimensional, expensive, and potentially multi-fidelity optimisation algorithms.

DRMPC_nonlinear_system_via_linearization

Expensive-Black-Box-Optim-ChemEng

OptiFunctions

Set of functions commonly used to test optimization algorithms

GP-MA

Source code for Gaussian process modifier adaptation (GP-MA)

cuatro

auto_discov_kin_rate_models

Complete code and data for reproducibility of results presented in paper.

simpleTorch

This repo aims to train neural networks with pytorch. The data are normalized insede the class. The validation is performed automatically. The output model gets the unscaled data and returns the output unscaled (The scaling is performed inside). This way the user do not interact the scaling,; however the user can select to not use the default scaling and scale the data before the training

DD-Hierarchical

Exploring data-driven techniques for the hierarchical integration of planning, scheduling, and control

humbo