Laetitia MOTTET

Laetitia MOTTET

Imperial College London

H-index: 14

Europe-United Kingdom

About Laetitia MOTTET

Laetitia MOTTET, With an exceptional h-index of 14 and a recent h-index of 13 (since 2020), a distinguished researcher at Imperial College London, specializes in the field of Porous media, Urban Environment, Indoor-outdoor modelling, Transport of Pollution, Ventilation.

His recent articles reflect a diverse array of research interests and contributions to the field:

Heat and mass transfer in the porous wick of a capillary evaporator

Indoor Geometry Generator (IGG) Manual

Fluidity Manual for indoor-outdoor exchanges and urban environment simulations

Data Assimilation with Machine Learning for Dynamical Systems: Modelling Indoor Ventilation

ENHANCING CFD SIMULATIONS OF COVID-19 DIFFUSION BY COUGHING AND SNEEZING USING DATA ASSIMILATION

Merging Real Images with Physics Simulations via Data Assimilation

Data assimilation in the latent space of a convolutional autoencoder

Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

Laetitia MOTTET Information

University

Position

and University of Cambridge Post-Doc.

Citations(all)

800

Citations(since 2020)

711

Cited By

307

hIndex(all)

14

hIndex(since 2020)

13

i10Index(all)

15

i10Index(since 2020)

14

Email

University Profile Page

Google Scholar

Laetitia MOTTET Skills & Research Interests

Porous media

Urban Environment

Indoor-outdoor modelling

Transport of Pollution

Ventilation

Top articles of Laetitia MOTTET

Heat and mass transfer in the porous wick of a capillary evaporator

arXiv preprint arXiv:2310.16426

2023/10/25

Laetitia Mottet
Laetitia Mottet

H-Index: 10

Indoor Geometry Generator (IGG) Manual

arXiv preprint arXiv:2310.15889

2023/10/24

Laetitia Mottet
Laetitia Mottet

H-Index: 10

Fluidity Manual for indoor-outdoor exchanges and urban environment simulations

arXiv preprint arXiv:2310.15923

2023/10/24

Laetitia Mottet
Laetitia Mottet

H-Index: 10

Data Assimilation with Machine Learning for Dynamical Systems: Modelling Indoor Ventilation

2023/1/13

ENHANCING CFD SIMULATIONS OF COVID-19 DIFFUSION BY COUGHING AND SNEEZING USING DATA ASSIMILATION

High Performance Computing on CRESCO Infrastructure: research activity and results 2020

2021/12

Merging Real Images with Physics Simulations via Data Assimilation

2021/8/30

Data assimilation in the latent space of a convolutional autoencoder

2021/6/9

Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

arXiv preprint arXiv:2104.06297

2021/4/13

Variational gaussian process for optimal sensor placement

Applications of Mathematics

2021/4

Natural ventilation in warm climates: The challenges of thermal comfort, heatwave resilience and indoor air quality

2021/3/1

Data assimilation in the latent space of a neural network

arXiv preprint arXiv:2012.12056

2020/12/22

Turbulent flows and pollution dispersion around tall buildings using adaptive large eddy simulation (LES)

Buildings

2020/7/10

The Hot Summer-Cold Winter region in China: challenges in the low carbon adaptation of residential slab buildings to enhance comfort.

Energy and Buildings

2020/5/26

Weak constraint Gaussian processes for optimal sensor placement

Journal of Computational Science

2020/4/1

A domain decomposition reduced order model with data assimilation (dd-roda)

Parallel Computing: Technology Trends

2020/1/1

Adaptive Domain Decomposition for Effective Data Assimilation

2020

See List of Professors in Laetitia MOTTET University(Imperial College London)