Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks

2020 - DKE Green, F Rindler - arXiv preprint

arXiv:2005.13028

Probabilistic time series prediction using Machine Learning.

Model Inference for ordinary differential equations by parametric polynomial kernel regression

2019 - DKE Green, F Rindler - 3rd ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering

doi:10.7712/120219.6340.18533 | arXiv:1908.02105

Prediction of a chaotic dynamical system from prior time series observations.

Probabilistic analysis for computational mechanics with applications in Civil Engineering

2017 - DKE Green - UNSW PhD Thesis

UNSWorks

Demonstrating a probability threshold problem using a Gaussian mixture model.

Efficient Markov Chain Monte Carlo for combined Subset Simulation and nonlinear finite element analysis

2017 - DKE Green - Computer Methods in Applied Mechanics and Engineering

doi:j.cma.2016.10.012

Comparing performance of Markov Chain Monte Carlo and the Spectral Stochastic Finite Element Method for predicting the probability of a soil footing collapse.

The simulation and discretisation of random fields for probabilistic finite element analysis of soils using meshes of arbitrary triangular elements

2015 - DKE Green, K Douglas, G Mostyn - Computers and Geotechnics

doi:10.1016/j.compgeo.2015.04.004

Stress and strain analysis of a slope with probabilistically modelled strengths.

Earthquake stability assessment for open pit mine slopes

2013 - JCW Toh, DKE Green, GE Swarbrick, MJ Fowler, BE Estrada - International Symposium on Slope Stability in Open Pit Mining and Civil Engineering

doi:10.36487/ACG_rep/1308_84_Toh

A cross section of the large open-pit mine simulated and analysed in this paper.

Model inference for Ordinary Differential Equations by parametric polynomial kernel regression

2019 - DKE Green - UNCECOMP 2019 - Crete, Greece

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Relative errors for different regression methods used in the presentation.

Aspects of Machine Learning for Engineering

2018 - DKE Green - University of Warwick - Coventry, UK

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Understanding parametric Machine Learning models in terms of probabilistic numerics and information entropy.

Machine Learning for Infrastructure Monitoring

2018 - DKE Green - The Alan Turing Institute - London, UK

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Slide discussing how clustering algorithms can be used for anomaly detection.

Probabilistic Computational Mechanics and Uncertainty Quantification: Rare event simulation and surrogate models

2017 - DKE Green - TU Braunschweig & TU Berlin - Germany

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Discussing the use of Deep Neural Networks for time series prediction on a PDE model.

Deep networks in uncertainty quantification: Artificial Neural Networks and Machine Learning

2017 - DKE Green - TU Braunschweig & TU Berlin - Germany

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Backpropagation in Artificial Neural Networks.

Lecture Slides for CVEN3203: Applied Geotechnics

2016 & 2017 - DKE Green - University of New South Wales - Sydney, Australia

Lecture 1: Introduction to Geotechnical Design

Lecture 2: Soil Mechanics review

Lecture 3: Bearing capacity of shallow foundations

Lecture 4: Settlement of shallow foundations

Lecture 5: Pile foundations

Lecture 6: Lateral earth pressure

Lecture 7: Retaining walls

Partially constructed concrete retaining wall.

Probabilistic analysis in computational mechanics with applications in Civil Engineering

2016 - DKE Green - Postgraduate Research Symposium - University of New South Wales - Sydney, Australia

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Uncertainty propagation in numerical models.

Markov Chain Monte Carlo for Rare Event Reliability Analysis with Nonlinear Finite Elements

2016 - DKE Green - SIAM Conference on Uncertainty Quantification - EPFL - Lausanne, Switzerland

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Probabilistic prediction of rare events using Markov Chain Monte Carlo - discussing convergence of different simulation methods.