A machine learning architecture for including wave breaking in envelope-type wave models

Related publications (27)

About
Privacy
Disclaimer

Graph Chatbot

Temporal Prediction of Landslide-Generated Waves Using a Theoretical-Statistical Combined Method

Christophe Ancey, Zhenzhu Meng, Yating Hu

For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship be ...

MDPI2023

Nonlocally-resonant metamaterials

Aleksi Antoine Bossart

In this thesis, we unveil a third design path to manipulate elastic waves within architected media, distinct from the traditional phononic crystal and locally-resonant metamaterial concepts. The core innovation lies in the concept of nonlocal resonances, d ...

EPFL2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Aiming beyond slight increases in accuracy

Daniel Probst

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability wil ...

NATURE PORTFOLIO2023

Natural slicks on Lake Geneva: From small-scale effects to large-scale formation and distribution

Mehrshad Foroughan

Low wind speeds (< 4 m/s) are ubiquitous in many water bodies, yet the physical processes occurring at the air-water interface in this range are poorly understood. A notable example is smooth patches on the water surface, known as natural slicks, formed wh ...

EPFL2023

Dissipative Solitons and Switching Waves in Dispersion-Modulated Kerr Cavities

Tobias Kippenberg, Rui Ning Wang, Aleksandr Tusnin, Alisa Davydova, Miles Henry Anderson, Alexey Tikan

We theoretically and experimentally investigate the formation of dissipative coherent structures in Kerr nonlinear optical microresonators, whose spectrum encompasses an integrated dispersion that exceeds the cavity free-spectral range. We are able to acce ...

American Physical Society2023

A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks

Berend Smit, Hyunsoo Park

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine ...

NATURE PORTFOLIO2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

3D Printed Acoustically Programmable Soft Microactuators

Mahmut Selman Sakar, Murat Kaynak, Amit Yedidia Dolev

The concept of creating all-mechanical soft microrobotic systems has great potential to address outstanding challenges in biomedical applications, and introduce more sustainable and multifunctional products. To this end, magnetic fields and light have been ...

2023

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

David Atienza Alonso, Tomas Teijeiro Campo, William Andrew Simon, Una Pale

The HyperDimensional Computing (HDC) Machine Learning (ML) paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other ML approaches, necessitat ...

Association for Computing MachineryNew YorkNYUnited States2022

Page 2 of 3

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

EPFL2023

3D Printed Acoustically Programmable Soft Microactuators

Mahmut Selman Sakar, Murat Kaynak, Amit Yedidia Dolev

2023

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

David Atienza Alonso, Tomas Teijeiro Campo, William Andrew Simon, Una Pale

Association for Computing MachineryNew YorkNYUnited States2022