Sensorless Position Detection Framework for a Multi-state Switched Reluctance Actuator of a Textile Machine

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Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques

Kamiar Aminian, Farzin Dadashi, Fabien Massé, Mahdi Hamidi Rad, Vincent Gremeaux

Comprehensive monitoring of performance is essential for swimmers and swimming coaches to optimize the training. Regardless of the swimming technique, the swimmer passes various swimming phases from wall to wall, including a dive into the water or wall pus ...

FRONTIERS MEDIA SA2021

Improving sample and feature selection with principal covariates regression

Michele Ceriotti, Edgar Albert Engel, Benjamin Aaron Helfrecht, Sergei Kliavinek

Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we ...

2021

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas, Stamatia Zoi Pagoulatou

In-vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thick ...

2021

Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence

Vasiliki Bikia

In a progressively aging population, it is of utmost importance to develop reliable, non-invasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Clinical parameters directly measured in the heart or the aort ...

EPFL2021

Page 3 of 3

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

AMER CHEMICAL SOC2021

Improving sample and feature selection with principal covariates regression

Michele Ceriotti, Edgar Albert Engel, Benjamin Aaron Helfrecht, Sergei Kliavinek

2021

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas, Stamatia Zoi Pagoulatou

2021

Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence

Vasiliki Bikia

EPFL2021

Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques

Kamiar Aminian, Farzin Dadashi, Fabien Massé, Mahdi Hamidi Rad, Vincent Gremeaux

FRONTIERS MEDIA SA2021