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Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning
Publications associées (56)
Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation
Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...
EPFL2024Digital Twins of Stone Masonry Buildings for Damage Assessment
Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero
Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the field of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...
Springer2024Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?
Machine learning techniques have been extensively developed in the field of electricity theft detection. However, almost all typical models primarily rely on electricity consumption data to identify fraudulent users, often neglecting other pertinent househ ...
Ieee-Inst Electrical Electronics Engineers Inc2024Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions
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 ...
EPFL2023Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...
EPFL2023From Kernel Methods to Neural Networks: A Unifying Variational Formulation
The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...
New York2023Aiming beyond slight increases in accuracy
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 PORTFOLIO2023Robustness of Local Predictions in Atomistic Machine Learning Models
Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Chiheb Ben Mahmoud
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...
Washington2023The Virtue of Complexity in Return Prediction
Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in ...
Hoboken2023