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Deep learning accelerated prediction of the permeability of fibrous microstructures

Publications associées (31)

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Frédéric Kaplan, Isabella Di Lenardo, Rémi Guillaume Petitpierre, Beatrice Vaienti

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

2023

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

Artificial intelligence for natural product drug discovery

Philippe Schwaller, Albert Hofstetter, Michael Alexander Skinnider, Florian Huber

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Nature Portfolio2023

MaxwellNet: Physics-driven deep neural network training based on Maxwell's equations

Demetri Psaltis, Joowon Lim

Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell's equations using computational electromagnetic simulations plays a critical role in understanding light-matter interaction and designing optic ...

AIP Publishing2022

Multiscale microshaping of functionally graded polymer-derived SiCN ceramics

Lorenz Hagelüken

Polymer-derived ceramics (PDCs) exhibit excellent properties and are compatible with many shaping techniques due to the liquid character of the preceramic polymers (PCPs). Furthermore, their properties are adjustable by modifying the precursor composition ...

EPFL2022

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Edoardo Remelli

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

EPFL2022

Light-Based Printing of Leachable Salt Molds for Facile Shaping of Complex Structures

Cyril Dénéréaz

3D printing is a powerful manufacturing technology for shaping materials into complex structures. While the palette of printable materials continues to expand, the rheological and chemical requisites for printing are not always easy to fulfill. Here, a uni ...

Wiley-V C H Verlag Gmbh2022

Capillary Effects in Fiber Reinforced Polymer Composite Processing: A Review

Véronique Michaud, Baris Çaglar, Jacobus Gerardus Rudolph Staal, Helena Luisa Teixido Pedarros

Capillarity plays a crucial role in many natural and engineered systems, ranging from nutrient delivery in plants to functional textiles for wear comfort or thermal heat pipes for heat dissipation. Unlike nano- or microfluidic systems with well-defined por ...

FRONTIERS MEDIA SA2022

Photocured Nanocellulose Composites: Recent Advances

Yves Leterrier, Aigoul Schreier, Mohammed Arif Poothanari, Julien Bras

This perspective gives an overview of recent progress in thefield of photocured polymer composites based on variousforms of nanocelluloses, with a focus on cellulose nanofibrils. The manufacturing processes and performances of these nanocellulosecomposites ...

AMER CHEMICAL SOC2022

Dielectric properties of 3-3 flexible composites by infiltration of elastomers into porous ceramic structures using cellulose scaffold

Frank Nüesch, Thomas Geiger

Elastomer composites are prepared by infiltrating polydimethylsiloxane (PDMS) into a porous ceramic structure of nanoparticles. This method differs from the conventional approach, where particles are dispersed into the polymer matrix, since here, the polym ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Drops: Controlled crystallization of organic crystals and their use as matrix materials for encapsulation of volatiles

Aysu Ceren Okur

Encapsulation techniques open up new possibilities to control the kinetics and location of the release of active ingredients. Despite the progresses achieved to obtain a better control over the dimensions and composition of the capsules, the encapsulation ...

EPFL2022