78 skills found · Page 3 of 3
etsap-TIMES / TIMES DemoTIMES Demo Model
JBris / Model Calibration EvaluationEvaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
jah1994 / PyTorchDIAPyTorchDIA is a Difference Image Analysis tool. It is built around the PyTorch machine learning framework, and uses automatic differentiation and (optional) GPU support to perform lightning fast optimisations of image models.
energyville / Modestomodesto is the Multi-Objective District Energy Systems Toolbox for Optimization. It is a python package that allows to easily set up optimisation models for district energy systems, solve these problems and post-process the results.
tum-ewk / FicusA (mixed integer) linear optimisation model for local energy systems
compops / Gpo Smc AbcBayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
bpfaff / FRAPOAccompanying package of the book 'Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
huanfachen / Open Source Location Cover ModelsCode of open source spatial optimisation and location cover models
tum-ens / Mathprog Energy ModelsCollection of educational mathematical optimisation models written in GNU MathProg
appliedcomputingtech / Pro GymProcess Simulations Meet AI. Supercharge Your Process Engineering. Generate Infinite Data, Train Advanced Models, and Revolutionise Industrial Optimisation with One Toolkit. From Flowsheet to AI-Powered Plant in Record Time 🚀
pngts / Nonlinear Parameter Estimation In Thermodynamic ModelsThe reliable solution of nonlinear parameter estimation problems is an essential computational and mathematical problem in process systems engineering, both in on-line and off-line applications. Parameter estimation in semi-empirical models for vapor – liquid equilibrium (VLE) data modelling plays an important role in design, optimization and control of separation units. Conventional optimisation methods may not be reliable since they do not guarantee convergence to the global optimum sought in the parameter estimation problem. In this work we demonstrate a technique, based on genetic algorithms (GA), that can solve the nonlinear parameter estimation problem with complete reliability, providing a high probability that the global optimum is found. Two versions of stochastic optimization techniques are evaluated and compared for nine vapour - liquid equilibrium problems: our genetic base algorithm and a hybrid algorithm. Reliable experimental data from the literature on vapor - liquid equilibrium systems were correlated using the UNIQUAC equation for activity coefficients. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. Considering that new globally optimal parameter values are found by using the proposed method we can surmise by our results that several sets of parameter values published in the DECHEMA VLE Data Collection correspond to local instead of global minima.
sayannath / TensorFlow NotebooksMy Tensorflow Notebook. In this notebooks I have implemented various kind of model optimisation techniques.
SuienS / Manim TrainerA toolkit for fine-tuning Large Language Models (LLMs) to generate Manim animation code using Supervised Fine-Tuning (SFT) and Visually Grounded Reinforcement Learning using Group Relative Policy Optimisation (GRPO/GSPO) techniques.
harmanpa / OptimisersModelica model-based optimisation library
GLechevalier / OpenGalateaAn open-source MCP server that gives Claude full control over your Prusa 3D printer — from finding a model online to slicing and printing it, entirely hands-free. Future works include orchestrating N printers for production optimisation.
juanjogg1987 / Fully Natural Gradient HetMOGPThis is a repositoy with a Fully Natural Gradient Scheme for Improving optimisation of the HetMOGP Model
JOS-RE / Financial AnalyticsAn end-to-end financial analytics platform that brings together portfolio optimisation, algorithmic trading, volatility modelling, and advanced econometric analysis. Built with a modular architecture, FINA is designed for academic use, research exploration, and open-source extensibility.
garg-akash / ModelPredictiveControllerTrajectory Optimisation using Model Predictive Controller (MPC)
qualifiedleads / Xandr UiA web UI for AppNexus stats reporting, optimisation and data modelling
energy-modelling-club / TIMES DK COMETSImported from gitlab.windenergy.dtu.dk/esy/times-dk_comets
