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Found 8 papers, 0 papers with code
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS).
Interpretable learning of effective dynamics for multiscale systems
In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data.
Hybrid data driven/thermal simulation model for comfort assessment
A benchmarking study is realized to compare different machine learning methods.
Rail Crack Propagation Forecasting Using Multi-horizons RNNs
Traditional methods rely on physical models and empirical equations such as Paris law, which often have limitations in capturing the complex nature of crack growth.
Meta-Learning for Airflow Simulations with Graph Neural Networks
To this end, we present a meta-learning approach to enhance the performance of learned models on OoD samples.
Continuous Methods : Adaptively intrusive reduced order model closure
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications.
Continuous Methods : Hamiltonian Domain Translation
This paper proposes a novel approach to domain translation.
CD-ROM: Complemented Deep-Reduced Order Model
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems.
