Prof Francesco Montomoli

Prof Francesco Montomoli

Imperial College London

H-index: 23

Europe-United Kingdom

About Prof Francesco Montomoli

Prof Francesco Montomoli, With an exceptional h-index of 23 and a recent h-index of 21 (since 2020), a distinguished researcher at Imperial College London, specializes in the field of Uncertainty Quantification, Machine Learning, Industry 4.0, Additive Manufacturing.

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

Investigation of Compressor Cascade Flow Using Physics-Informed Neural Networks with Adaptive Learning Strategy

Aleatory uncertainty quantification based on multi-fidelity deep neural networks

SeAr PC: Sensitivity Enhanced Arbitrary Polynomial Chaos

On the pressure wave emanating from a deflagration flame front

High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks

Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park

Investigation of compressor cascade flow based on physics-informed neural networks

A multiscale strategy for fouling prediction and mitigation in gas turbines

Prof Francesco Montomoli Information

University

Position

Reader - UK

Citations(all)

1979

Citations(since 2020)

1259

Cited By

1178

hIndex(all)

23

hIndex(since 2020)

21

i10Index(all)

52

i10Index(since 2020)

38

Email

University Profile Page

Imperial College London

Google Scholar

View Google Scholar Profile

Prof Francesco Montomoli Skills & Research Interests

Uncertainty Quantification

Machine Learning

Industry 4.0

Additive Manufacturing

Top articles of Prof Francesco Montomoli

Title

Journal

Author(s)

Publication Date

Investigation of Compressor Cascade Flow Using Physics-Informed Neural Networks with Adaptive Learning Strategy

AIAA Journal

Li Zhihui

Francesco Montomoli

Sanjiv Sharma

2024

Aleatory uncertainty quantification based on multi-fidelity deep neural networks

Reliability Engineering & System Safety

Zhihui Li

Francesco Montomoli

2024/5/1

SeAr PC: Sensitivity Enhanced Arbitrary Polynomial Chaos

arXiv preprint arXiv:2402.05507

Nick Pepper

Francesco Montomoli

Kyriakos Kantarakias

2024/2/8

On the pressure wave emanating from a deflagration flame front

Heliyon

V Bisio

F Montomoli

S Rossin

VL Tagarielli

2024/2/7

High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks

Journal of Turbomachinery

Zhihui Li

Francesco Montomoli

Nicola Casari

Michele Pinelli

2023/11/1

Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park

Scientific Reports

Christopher M Baker

Palma Blonda

Francesca Casella

Fasma Diele

Carmela Marangi

...

2023/9/4

Investigation of compressor cascade flow based on physics-informed neural networks

arXiv preprint arXiv:2308.04501

Zhihui Li

Francesco Montomoli

Sanjiv Sharma

2023/7/15

A multiscale strategy for fouling prediction and mitigation in gas turbines

Riccardo Friso

2023/4/6

Failure domain analysis using Sliced-Normal distributions

James Hammond

Luis G Crespo

Francesco Montomoli

2023

Reynolds Sensitivity of the Wake Passing Effect on a LPT Cascade Using Spectral/hp Element Methods

International Journal of Turbomachinery, Propulsion and Power

Andrea Cassinelli

Andrés Mateo Gabín

Francesco Montomoli

Paolo Adami

Raul Vázquez Díaz

...

2022/2/22

High-Order Spectral/hp Compressible and Incompressible Comparison of Transitional Boundary-Layers Subject to a Realistic Pressure Gradient and High Reynolds Number

Guglielmo Vivarelli

João Anderson Isler

Francesco Montomoli

Spencer J Sherwin

Paolo Adami

2022/6/13

Topology optimisation of turbulent flow using data-driven modelling

Structural and Multidisciplinary Optimization

James Hammond

Marco Pietropaoli

Francesco Montomoli

2022/2

Development of Machine-Learnt Turbulence Closures for Wake Mixing Predictions in Low-Pressure Turbines

Yuri Frey Marioni

Paolo Adami

Raul Vazquez Diaz

Andrea Cassinelli

Spencer Sherwin

...

2022/6/13

Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries

International Journal of Heat and Fluid Flow

James Hammond

Marco Pietropaoli

Francesco Montomoli

2022/12/1

Predictions and uncertainty quantification of the loading induced by deflagration events on surrounding structures

Process Safety and Environmental Protection

V Bisio

F Montomoli

S Rossin

M Ruggiero

VL Tagarielli

2022/2/1

Thermal Management for Electrification in Aircraft Engines: Optimization of Coolant System

N Raske

O Ausin Gonzalez

S Furino

M Pietropaoli

S Shahpar

...

2022/6/13

Adaptive learning for reliability analysis using support vector machines

Reliability Engineering & System Safety

Nick Pepper

Luis Crespo

Francesco Montomoli

2022/10/1

Acknowledgment to Reviewers of IJTPP in 2021

Int. J. Turbomach. Propuls. Power

Aki Grönman

Giovanna Cavazzini

Andrea Cattanei

Gwi Bo Byun

Andrea Ferrero

...

2022

Error quantification for the assessment of data-driven turbulence models

Flow, Turbulence and Combustion

James Hammond

Yuri Frey Marioni

Francesco Montomoli

2022/6

A Non-Parametric Histogram Interpolation Method for Design Space Exploration

Journal of Mechanical Design

Nick Pepper

Francesco Montomoli

Sanjiv Sharma

2022/8/1

See List of Professors in Prof Francesco Montomoli University(Imperial College London)

Co-Authors

H-index: 70
Spencer J. Sherwin

Spencer J. Sherwin

Imperial College London

H-index: 34
Michele Pinelli

Michele Pinelli

Università degli Studi di Ferrara

H-index: 31
Sylvain Laizet

Sylvain Laizet

Imperial College London

H-index: 30
Vito L Tagarielli

Vito L Tagarielli

Imperial College London

H-index: 29
Professor Emeritus  Francesco Martelli

Professor Emeritus Francesco Martelli

Università degli Studi di Firenze

H-index: 25
Kam Chana

Kam Chana

University of Oxford

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