SpringNuance

Abaqus-Fortran-Subroutine

Resources collected from various sources for subroutine documentations

Abaqus-Subroutine-References

This repository contains the subroutine source code of Emilio Martínez-Pañeda on various physical problems. This is his website https://www.empaneda.com/codes/

Bayesian-Data-Analysis-Project

Computational-Social-Science

DAMASK3-Processing-Project

Dungeon-Crawler

Fracture-Mechanics

Gaussian-Processes

The course covers overview of Gaussian processes in machine learning, and provides both a theoretical and practical background for leveraging them. Specifically, it covers Gaussian process regression, classification, unsupervised modelling, as well as a selection of more recent specialized topics.

Numerical-Methods-In-Engineering

This course covers the theory behind classical numerical methods (for example: Newton-Raphson, Runge-Kutta and LU factorisation), and uses Matlab as a tool to solve, analyse and visualise computational problems and data. There is correction and improvement of existing code, while the validity and accuracy of numerical predictions are reflected

VUMAT-Abaqus-ML-Integration

SpringNuance/Abaqus-Fortran-Subroutine

Resources collected from various sources for subroutine documentations

SpringNuance/VUMAT-Abaqus-ML-Integration

SpringNuance/Abaqus-Subroutine-References

This repository contains the subroutine source code of Emilio Martínez-Pañeda on various physical problems. This is his website https://www.empaneda.com/codes/

SpringNuance/Numerical-Methods-In-Engineering

This course covers the theory behind classical numerical methods (for example: Newton-Raphson, Runge-Kutta and LU factorisation), and uses Matlab as a tool to solve, analyse and visualise computational problems and data. There is correction and improvement of existing code, while the validity and accuracy of numerical predictions are reflected

SpringNuance/Business-Analytics-II

This course covers simulation, decision trees, value of information, expected utility theory, risk attitudes, stochastic dominance, risk measures, multi-attribute utility/value theory, modelling uncertainty and multiple objectives in optimization problems.

SpringNuance/Deep-Learning

This course covers the general principles of deep learning, and the central deep learning methods discussed in the course, such as MLP, Recommender System, RNN, Transformers, GAN, autoencoders, diffusion generative model, autoregressive model and fewshot classification

SpringNuance/Gaussian-Processes

The course covers overview of Gaussian processes in machine learning, and provides both a theoretical and practical background for leveraging them. Specifically, it covers Gaussian process regression, classification, unsupervised modelling, as well as a selection of more recent specialized topics.

SpringNuance/Machine-Learning-Advanced-Probabilistic-Methods

The course covers concepts in probabilistic machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, latent linear models, and algorithms for exact and approximate inference, with an emphasis on variational inference, which helps derive approximate inference algorithms for complex models

SpringNuance/Multivariate-Statistical-Analysis

This course includes multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), canonical correlation analysis, discriminant analysis, statistical depth functions, classification and clustering. Software R is used in the exercises

SpringNuance/Production-Systems-Modelling

This course covers queuing networks, optimization, regression analysis, and neural networks. The application of the methods to production systems planning and control: Hierarchical production planning, cost functions, Little's law, scheduling, lot sizes and set-ups, capacity planning, aggregate planning, facility location

SpringNuance/Abaqus-UEL-von-Mises-plasticity

This is the template for von Mises plasticity using UEL subroutine. It returns the exact result as *ELASTIC and *PLASTIC

SpringNuance/Business-Intelligence

This course covers the key building blocks of BI, such as data management, data warehousing, analytic tools, data mining, and reporting. The course provides both a hands-on practical and a theoretical approach to learning about data driven decision-making and analytical problem solving.

SpringNuance/CrystalPlasticity

CP UMAT and CZM UEL for Abaqus

SpringNuance/Federated-Learning

This course covers basic charecteristics of decentralized data, basic machine learning models for decentralized data, implementation of simple federated learning algorithms and analysis of computational and statistical properties of federated learning algorithms

SpringNuance/Human-In-The-Loop-De-Novo-Molecular-Design

Reinforcement Learning (RL) has shown promise in advancing molecular design and therapeutic research. This project proposes a model of human-AI interaction for reward learning in molecular optimization

SpringNuance/Introduction-To-Artificial-Intelligence

This course covers an overview of various applications of AI and of the fundamental problems, methods, and algorithms that underlie and that are necessary to build AI applications.

SpringNuance/Material-Modelling-In-Civil-Engineering

This course covers fundamentals of material modelling within the framework of continuum mechanics, such as physical and mathematical description of key features of common material behaviour in civil engineering related to their thermo-mechanical response. Computational tools commonly used in material modelling in civil engineering are also covered

SpringNuance/Mechanical-Testing-Of-Materials

This course covers measurement of force, displacement, and strain, loadframes, actuators; and grips quasi-static, dynamic, and cyclic loading; selected special challenges in mechanical testing; digital image correlation and other full-field measurement techniques. It also covers introduction inverse problem methodologies in experimental mechanics

SpringNuance/Special-Course-Large-Scale-Computing-And-Data-Analysis

Collection of reference projects from the large scale computing course

SpringNuance/SpringNuance

SpringNuance/Statistical-Natural-Language-Processing

This course covers language modeling, machine translation, text mining, speech recognition, chatbots and related areas to process natural language contents. Furthermore, it covers basic methods and techniques used for statistical natural language modeling including, for instance, clustering, classification, generation and HMM

SpringNuance/Convex-Optimization

SpringNuance/deepxde

A library for scientific machine learning and physics-informed learning

SpringNuance/flower-federated-learning-framework

Flower: A Friendly Federated Learning Framework

SpringNuance/Jishulink-Materials

SpringNuance/Partial-Differential-Equations

SpringNuance/SNLP-Project

This project aims to benchmark many variants of BERT model on two GLUE tasks: QQP and SST-2 and report their respective performance to find the best and efficient BERT model

SpringNuance/Software-Testing-and-Quality-Assurance

SpringNuance/Speech-Processing

SpringNuance/Universal-Optimization-Platform

This project template is used to handle a generic optimization problem