Computational anatomy

Science de la nature
Biologie
Neurosciences
Neurosciences computationne...

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (29)

Robust optimization of control parameters for WEC arrays using stochastic methods

Tommaso Vanzan, Edie Miglio

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Journal of Computational Physics2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

Mathieu Salzmann, Jiancheng Yang, Kun Sun, Yunhua Zhang

3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle t ...

Ieee Computer Soc2023

How deep convolutional neural networks lose spatial information with training

Matthieu Wyart, Umberto Maria Tomasini, Francesco Cagnetta, Leonardo Petrini

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

Iop Publishing Ltd2023

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Guilherme Migliato Marega

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

EPFL2023

Design, computational and experimental investigation of a small-scale turbopump for organic Rankine cycle systems

Jürg Alexander Schiffmann, Sajjad Zakeralhoseini

The hydraulic design, computational analysis, and experimental investigations of a high-speed small-scale turbopump for mobile waste heat recovery applications based on organic Rankine cycle systems are presented in this paper. Such applications demand hig ...

Elsevier2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, James Gonzalo King, Rajnish Ranjan, Armando Romani, Mustafa Anil Tuncel, Aleksandra Zuzanna Teska, Polina Litvak, Matthias Wolf, Felix Schürmann, Daniel Keller, Werner Alfons Hilda Van Geit, Pramod Shivaji Kumbhar, Alexis Arnaudon, Jean-Denis Georges Emile Courcol, Giuseppe Chindemi, András Ecker, Michael Emiel Gevaert, Christian Andreas Rössert, Vishal Sood, Sirio Bolaños Puchet, James Bryden Isbister, Judit Planas Carbonell, Daniela Egas Santander, Omar Awile, Maria Reva, Joni Henrikki Herttuainen, Genrich Ivaska, Natali Barros Zulaica, Samuel Lieven D. Lapere, Christoph Pokorny, Elvis Boci, Thomas Brice Delemontex, Jorge Blanco Alonso, Tanguy Pierre Louis Damart, Alexander Dietz, Darshan Mandge, ,

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Page 2 sur 2

Gianluca Ficarelli

Weina Ji

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

2024