34 skills found · Page 1 of 2
EmuKit / EmukitA Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Fusion-Power-Plant-Framework / BluemiraBluemira is an integrated inter-disciplinary design tool for future fusion reactors. It incorporates several modules, some of which rely on other codes, to carry out a range of typical conceptual fusion reactor design activities.
dpad / OrbitalTrajectories.jlOrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.
telstra / Open KildaOpenKilda is an open-source OpenFlow controller initially designed for use in a global network with high control-plane latency and a heavy emphasis on latency-centric data path optimisation.
shariethernet / Physical Design With OpenLANE Using SKY130 PDKThis project is done in the course of "Advanced Physical Design using OpenLANE/Sky130" workshop by VLSI System Design Corporation. In this project, a PicoRV32a SoC is taken and then the RTL to GDSII Flow is implemented with Openlane using Skywater130nm PDK. Custom-designed standard cells with Sky130 PDK are also used in the flow. Timing Optimisations are carried out. Slack violations are removed. DRC is verified
Ryan-Rhys / Constrained Bayesian Optimisation For Automatic Chemical DesignCode to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a
xnacly / Purple Gardenpurple garden is a lean scripting language designed for performance, with aggressive optimisations, JIT compilation, fine-grained memory control, and optional garbage collection.
pimlphm / Physics Informed Machine Learning Based On TCNA hybrid approach using physical information (PI) lightweight temporal convolutional neural networks (PI-TCN) for remaining useful life (RUL) prediction of bearings under stiffness degradation. It consists of three PI hybrid models: a) PI feature model (PIFM) - constructs physical information health indicators (PIHI) to increase the feature space; b) PI layer model (PILM) - encodes the physics governing equations in a hidden layer; c) PI layer-based loss model (PILLM) - designs PI conflicting losses, taking into account the integration of the physics input-output relationship module into the differences before and after the loss function. I have provided the original model and basic methodology here and welcome further optimisation of the structure and associated training methods. Interestingly, it is not the number of layers of physics knowledge that is more useful; the right structure for the right physics knowledge is the key to success. Similar to pure DL tuning, to design neural networks based on full physical knowledge is a direction that I am very interested in and would like to discuss with you.
qingfengxia / CAE Pipelinedemonstration of automated engineering design and optimisation process uisng open source tools and Python
aschowtjak / ADAPTADAPT is designed for the inverse parameter identification of constitutive material models using mathmatical optimisation. It is designed to work with finite element simulations but its modular implementation offers an interface to basically any simulation framework. The tool is able to use global data such as forces as well as local data such as strain or displacement fields for the model calibration. The latter one is based on a robust interpolation scheme for irregular data sets and meshes/query points.
daptablade / Parametric Cgx ModelParametric shell half-wing model geometry creation and meshing in CGX, followed by CCX analysis, parametric design studies and optimisation using OpenMDAO
ami-iit / Paper Bergonti 2024 Icra Codesign Morphing DronesSupplementary Material "Co-Design Optimisation of Morphing Topology and Control of Winged Drones" published in IEEE 2024 International Conference on Robotics and Automation (ICRA)
nmonette / NCC UEDOfficial Implementation of `An Optimisation Framework for Unsupervised Environment Design` from RLC 2025
Shahriar-0 / Neural Networks And Deep Learning Course Projects S2024neural network and deep learning course projects to work and design on different problems such as classification, regression, optimisation and much more
Jatin-EM / AntennaarrayCodes for antenna array design and optimisation
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.
trsav / Reactor BenchmarkA set of reactor design benchmark problems to evaluate high-dimensional, expensive, and potentially multi-fidelity optimisation algorithms.
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.
audreyt / Civic.AIA governance framework by Audrey Tang and Caroline Green that translates Joan Tronto's care ethics into six machine-codeable design primitives for Civic AI — bounded, place-specific AI stewards engineered for relational health, not unbounded optimisation.
thanosgoulas / 3D Printing FormulatorA standalone desktop application for the design and optimisation of ceramic resin and paste formulations for 3D printing. Developed independently by Dr Thanos Goulas.
