Data Centric Engineering and Data-Driven Modelling - Computational Engineering Lab Report 2019

Report (2019)

https://www.cambridge.org/core/journals/data-centric-engineering

DATA DRIVEN SURGICAL SIMULATIONS

Scientific Conference (2020, July)

The DAta Protection REgulation COmpliance Model

in IEEE Security and Privacy (2019), 17(6), 37-45

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for ... [more ▼]

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for semiautomatic processing of legal text and the leveraging of state-of-the-art legal informatics approaches, which are useful for legal reasoning, software design, information retrieval, or compliance checking. [less ▲]

Data Science in Europe

in International Journal of Data Science and Analytics (2018)

Data Science, Learning by Latent Structures, and Knowledge Discovery

Book published by Springer (2015)

Data-driven modelling and simulation: fracture and medical simulations

Presentation (2018, February 08)

Predicting failure in aircraft structures – simulating fracture across scales and times You could fly every day of your life in a commercial aircraft for twenty thousand years without suffering a fatal ... [more ▼]

Predicting failure in aircraft structures – simulating fracture across scales and times You could fly every day of your life in a commercial aircraft for twenty thousand years without suffering a fatal accident. This extraordinary level of safety is the product of decades of engineering and materials science research. Simultaneously, engineers have strived to produce lighter and stronger aircraft, with increased range and metals have thus been gradually replaced by lighter advanced composite materials which take up more than half of the structural weight of today's most advanced aircraft. Such progress has been largely enabled by modeling and simulation of materials and structures, which have revolutionized design by enabling engineers to investigate virtually various design strategies. This presentation will focus on the challenges which have been posed, are posed, and will be posed to such modeling and simulation tools in the strive to predict the durability of lighter, stronger, longer-ranging and more reliable aircraft. [less ▲]

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation

in Human Brain Mapping (2022)

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by ... [more ▼]

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage. [less ▲]

Dealing with interfaces in partitioned model order reduction for application to nonlinear problems

Scientific Conference (2013)

We propose a reduced order modelling technique based on a partitioning of the domain of study in the context of para- metric nonlinear problems. A formulation of the reduction of the displacement and of ... [more ▼]

We propose a reduced order modelling technique based on a partitioning of the domain of study in the context of para- metric nonlinear problems. A formulation of the reduction of the displacement and of the interface tractions linking subdomains to each others will be performed in a FETI context. [less ▲]

Decentralised minimal-time consensus

in The proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) (2011)

This study considers the discrete-time dynamics of a network of agents that exchange information according to the nearest-neighbour protocol under which all agents are guaranteed to reach consensus ... [more ▼]

This study considers the discrete-time dynamics of a network of agents that exchange information according to the nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the consensus value of the whole network in finite time using only the minimal number of successive values of its own history. We show that this minimal number of steps is related to a Jordan block decomposition of the network dynamics and present an algorithm to obtain the minimal number of steps in question by checking a rank condition on a Hankel matrix of the local observations. Furthermore, we prove that the minimal number of steps is related to other algebraic and graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the underlying graph topology. [less ▲]

Decentralised minimum-time consensus

in Automatica (2013), 49(5), 1227-1235

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically ... [more ▼]

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the final consensus value of the whole network in finite time using the minimum number of successive values of its own state history. We show that the minimum number of steps is related to a Jordan block decomposition of the network dynamics, and present an algorithm to compute the final consensus value in the minimum number of steps by checking a rank condition of a Hankel matrix of local observations. Furthermore, we prove that the minimum number of steps is related to graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the minimum external equitable partition. [less ▲]