Simulation of reacting flows using artificial neural networks: application to multi-regime combustion
Cédric Mehl and Damien Aubagnac-Karkar

Neural network-based filtered drag model for cohesive gas-particle flows
Josef Tausendschön, Stefan Radl and Sankaran Sundaresan

Using conservation laws to infer deep learning model accuracy of Richtmyer-Meshkov instabilities
Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry, Youngsoo Choi, Daniel A. White and Jonathan L. Belof

Deep Convolutional Autoencoders for Predicting Wind-Driven Spatial Patterns
M. Giselle Fernández-Godino, Donald D. Lucas and Qingkai Kong

Approximating the full-field temperature evolution in 3D electronic systems from randomized “Minecraft” systems
Monika Stipsitz and Helios Sanchis-Alepuz

Machine Learning in Topology Optimisation - Challenges and Prospects (Keynote Lecture)
Rebekka V. Woldseth, Niels Aage, J. Andreas Bærentzen and Ole Sigmund

Deep learning based dimensionality reduction for fracture mechanics
Krushna Shinde, Vincent Itier, José Mennesson, Dmytro Vasiukov and Modesar Shakoor

Mesh generation for finite element simulations with Deep Learning
Martin Legeland, Kevin Linka and Christian J. Cyron

Real-time large deformations: A probabilistic deep learning approach
Saurabh Deshpande, Jakub Lengiewicz and Stephane Bordas

An explainable pipeline for machine learning with functional data
Katherine Goode, J. Derek Tucker, Daniel Ries and Heike Hofmann

A PINN-based model for coupled hydro-poromechanics in reservoir simulations
Caterina Millevoi, Nicolò Spiezia and Massimiliano Ferronato

CoSTA: Improving physics-based models using deep learning
Sindre S. Blakseth, Adil Rasheed, Trond Kvamsdal and Omer San

Physics-informed neural networks applied to two-phase flow in porous media problems
John Hanna, Jose V. Aguado, Sebastien Comas-Cardona, Ramzi Askri and Domenico Borzacchiello

Towards understanding of boiling conjugate heat transfer using physics informed neural network
Robin Kamenicky, Konstantinos Ritos, Victorita Dolean, Jennifer Pestana, Katherine Tant and Salaheddin Rahimi

Deep learning model operating on graph structured data for assisting multiphase flows
George El Haber, Jonathan Viquerat, Aurelien Larcher, David Ryckelynck and Elie Hachem

Physics inspired neural network plasticity modeling
Knut Andreas Meyer and Fredrik Ekre

Development of the Defects Detection System for Carbon Fiber Reinforced Plastic by Using Infrared Stress Analysis and Machine Learning
Yuta Kojima, Kenta Hirayama, Katsuhiro Endo, Kazuya Hiraide, Mayu Muramatsu and Yoshihisa Harada

Health indicator learning for predictive maintenance based on a triplet loss and deep siamese network
Etienne Jules, Cecille Mattrand and Jean-Marc Bourinet

Understanding Vehicle Reliability and Safety with Multivariate Sensory Data: A Tire Wear Case Study
Thabang Lebese, Cécile Mattrand, David Clair, Jean-Marc Bourinet, François Deheeger and Rodrigue Decatoire

Application of multiresolution analysis and deep learning to obtain failure pressure of corroded pipelines
Adriano D. Marques Ferreira, Silvana M. Bastos Afonso and Ramiro B. Willmersdorf

Remaining Useful Life prediction with a Deep Self-Supervised Learning Approach
Anass Akrim, Christian Gogu, Rob Vingerhoeds and Michel Salaün

A framework for neural network based constitutive modelling of inelastic solid materials
Eugenio J. Muttio-Zavala, Reem Alhayki, Wulf G. Dettmer and Djordje Peric