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t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

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DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Fernando Perez Cruz, Luis Salamanca Miño, Firat Ozdemir

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

American Society of Mechanical Engineers (ASME)2024

Neural Exponential Stabilization of Control-affine Nonlinear Systems

Giancarlo Ferrari Trecate, Muhammad Zakwan

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Netw ...

Institute of Electrical and Electronics Engineers Inc.2024

Robust time series denoising with learnable wavelet packet transform

Olga Fink, Gaëtan Michel Frusque

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

2024

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Ana-Maria Cretu

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these ...

Association for Computing Machinery, Inc2024

On the different regimes of stochastic gradient descent

Matthieu Wyart, Antonio Sclocchi

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size B, and the step size or learning rate n. For small B and large n, SGD corresponds to a stochastic ...

National Academy of Sciences2024

Normal Contact Force Estimation Using Deep Learning

Yves Perriard, Paolo Germano, Xinxin Liao, Marc Michel Charles Favier

Small scale ultrasonic piezoelectric actuators performance strongly depends on not well-known contact dynamics. Deep Neural Network (DNN) sees their use in physic simulation growing as their flexibility allows better performance especially when dynamics la ...

Institute of Electrical and Electronics Engineers Inc.2024

Balancing computational chemistry's potential with its environmental impact

Philippe Schwaller, Oliver Tobias Schilter

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often ...

ROYAL SOC CHEMISTRY2024

Scalable semantic 3D mapping of coral reefs with deep learning

Anders Meibom, Devis Tuia, Guilhem Maurice Louis Banc-Prandi, Jonathan Paul Sauder

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existen ...

2024

Fast learning without synaptic plasticity in spiking neural networks

Guillaume Emmanuel Fernand Bellec

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to r ...

Nature Portfolio2024

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Saleh Baghersalimi, Farnaz Forooghifar

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

2024

Breaking Template Protection: Reconstruction of Face Images from Protected Facial Templates

Hatef Otroshi Shahreza

Face recognition systems tend toward ubiquity and are commonly utilized for security purposes. These systems operate based on facial representations, called templates, extracted by a deep neural network from each face image. However, it has been shown that ...

IEEE2024