5 papers
High Resolution Seismic Waveform Generation using Denoising Diffusion
Kadek Hendrawan Palgunadi, Andreas Bergmeister, Andrea Bosisio +5
Accurate prediction and synthesis of seismic waveforms are crucial for seismic-hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such a…
MeltpoolINR: Predicting temperature field, melt pool geometry, and their rate of change in laser powder bed fusion
Manav Manav, Nathanael Perraudin, Yunong Lin +4
We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt po…
Efficient and Scalable Graph Generation through Iterative Local Expansion
Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin +1
In the realm of generative models for graphs, extensive research has been conducted. However, most existing methods struggle with large graphs due to the complexity of representing…
From STEM-EDXS data to phase separation and quantification using physics-guided NMF
Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet +4
We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the…
Efficient algorithms for regularized Poisson Non-negative Matrix Factorization
Nathanaël Perraudin, Adrien Teutrie, Cécile Hébert +1
We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth fu…