- Published on 08 May 2023
While neural networks can help to improve the accuracy of fluid flow simulations, new research shows how their accuracy is limited unless the right approach is taken. By embedding fluid properties into neural networks, simulation accuracy can improve by orders of magnitude.
The Lattice Boltzmann Method (LBM) is a simulation technique used to describe the dynamics of fluids. Recently, there has been an increasing interest in employing neural networks for computational modelling of fluids. The results of a collaboration between researchers from Eindhoven University of Technology and Los Alamos National Laboratory, published in EPJ E, show how neural networks can be embedded into a LBM framework to model collisions between fluid particles. The team found that it is essential to embed the correct physical properties into the neural network architecture to preserve accuracy. These discoveries could deepen researchers’ understanding of how to model fluid flows.
- Published on 04 May 2023
Heating clusters of these elements reveals key differences
The bonds between clusters of elements in the fourteenth group of the periodic table are known to be fickle. Ranging from the nonmetal carbon, to the metalloids silicon and germanium, to the metals tin and lead, all these elements share the same configuration of valence electrons – electrons in their atoms’ outermost energy level. However, clusters formed from these elements respond differently to being excited with laser pulses. Studying the response of atomic clusters to photoexcitation as a function of the element they are composed of and their number of atoms reveals patterns that can be used to gain insight into their structure and binding mechanisms.
- Published on 04 May 2023
Austenitic steel is a potential material for nuclear fusion reactors
Producing energy on Earth through nuclear fusion, the type of reaction that powers the Sun, has proven to be a major challenge. The extreme conditions needed for such a reaction require the walls of a nuclear fusion device to be made of a material with a particular set of mechanical properties, including being able to withstand incredibly high temperatures and be shock- and corrosion-resistant. Austenitic steel, a non-magnetic steel with a crystalline structure, is one of the materials considered for use in nuclear fusion devices.
- Published on 27 April 2023
Models based on the principles of statistical physics can provide useful insights into how languages change through contact between speakers of different languages. In particular, the analysis reveals how unusual linguistic forms are more likely to be replaced by more regular ones over time.
The field of historical linguistics explores how languages change over time, with a particular focus on the evolution of sounds, meanings, and structures in words and sentences. So far, however, it hasn’t been widely studied from the viewpoint of statistical physics – which uses mathematical models to explain patterns and behaviours in complex, evolving systems. Through a series of models described in EPJ B, Jean-Marc Luck at Université Paris-Saclay, together with Anita Mehta at the Clarendon Institute in Oxford, use statistical physics to show how exceptions to well-established grammatical rules are linked to the influence of neighbouring languages.
- Published on 19 April 2023
By carefully structuring the data used to train models of complex systems by leveraging physics and information theory, researchers can significantly improve the quality of their predictions, without relying on additional principles from machine learning in situations where less information about the system is available.
Researchers are now increasingly driven to identify and model the intricate mathematical patterns found in complex natural systems, where the interactions of many simple parts and subsystems can give rise to deeply intricate mathematical patterns. Today, machine learning is the most widely used technique to model these systems. Through new analysis in EPJ E, a research team at Université Paris-Saclay shows how a ‘curriculum learning’ approach, which carefully structures the data used to train models, can significantly improve their results, without relying on additional machine learning principles.
EPJ Plus Focus Point Issue: Breakthrough Optics- and Complex Systems-based Technologies of Modulation of Drainage and Clearing Functions of the Brain
- Published on 13 March 2023
Guest Editors: Jürgen Kurths, Thomas Penzel, Valery Tuchin, Teemu Myllylä, Ruikang Wang, Oxana Semyachkina-Glushkovskaya
The treatment of brain diseases during sleep is a pioneering trend in modern medicine. This is due to new discoveries in the science of lymphatic "vessels-vacuums" that clean the brain during deep sleep. Today, sleep is considered as a novel biomarker and a promising therapeutic target for brain diseases associated with the drainage system injuries and the blood-brain barrier (BBB) leakage, including Alzheimer's and Parkinson's diseases, depression, brain trauma and intracranial hemorrhages. This issue presents multi-disciplinary approaches, including nonlinear signal processing analysis, maсhine learning technologies, modeling of the brain drainage system, optical methods, brave and innovative ideas and very promising experimental and clinical results focusing on the study of therapeutic and diagnostic properties of sleep as well as the development of novel strategies for the modulation of restorative sleep functions.
EPJ Web of Conferences Highlight - ESSENA11: 11th European Summer School on Experimental Nuclear Astrophysics
- Published on 10 March 2023
The European Summer School on Experimental Nuclear Astrophysics has run for more than 2 decades and brings together nuclear physicists and astrophysicists from major universities, laboratories and research facilities. It has been organized jointly by the INFN Laboratori Nazionali del Sud (Catania) and the Dipartimento di Fisica e Astronomia “E. Majorana” of the Catania University.
It is an opportunity to present novel work across the full range of both theoretical and experimental activities covering all novel aspects ranging from cosmology to stellar physics as well as nuclear aspects, methods and instruments related to investigations of nuclear reactions important for nuclear astrophysics.
- Published on 08 March 2023
The Scientific Advisory Committee of EPJ is delighted to welcome Professor Quentin Glorieux, as the new representative for the French Physical Society.
Quentin Glorieux is Associate Professor at Sorbonne Université, Laboratoire Kastler Brossel, and fellow member of Institut Universitaire de France (IUF). His expertise covers a broad range of topics from nanooptics to quantum gases and superfluidity. In the last years, his experimental work focus on Quantum Fluids of Light to simulate many-body physics and analogue gravity with light in various platforms (from exciton-polaritons in microcavities to non-linear propagation of light in atomic vapors.)
- Published on 08 March 2023
More than a decade has passed since the publication of the special issue “20 Years of Recurrence Plots: Perspectives for a Multi-purpose Tool of Nonlinear Data Analysis” in the European Physical Journal—Special Topics (EPJST). The hope for further developments inspired by the interesting contributions in this special issue was fully realized. We see an amazing development in the field of recurrence plots (RPs), recurrence quantification analysis (RQA), and recurrence networks. Recurrence analysis is not just one method; it has emerged as an entire framework with many extensions, special recurrence definitions, and specifically designed methods and tools. It has found spreading applications in diverse and growing scientific fields. Recurrence analysis has become a widely accepted concept, even referred to in studies that are actually not using it as a method, but rather using it as a reference or alternative tool. It continues to be an active area of research and development today. An attempt to provide an overview of the most significant technical developments of this recurrence-plot-based framework in the past decade is included in this special issue.
All articles are available here and are freely accessible until 8 May 2023. For further information read the Editorial by Norbert Marwan, Charles L. Webber & Andrzej Rysak ”Trends in recurrence analysis of dynamical systems” Eur. Phys. J. Spec. Top. 232, 1–3 (2023). https://doi.org/10.1140/epjs/s11734-023-00766-z.
- Published on 07 March 2023
A new simulation approach named eTLE aims to improve the precision of a primary tool for estimating neutron behaviours in 3D space. This study examines the approach in detail – validating its reliability in predicting the scattering of neutrons in crystalline media.
Tripoli-4® is a tool used by researchers to simulate the behaviours of interacting neutrons in 3D space. Recently, researchers developed a new ‘next-event estimator’ (NEE) for Tripoli-4®. Named eTLE, this approach aims to increase Tripoli-4®’s precision using Monte Carlo simulations: a class of algorithms which solve problems by repeatedly estimating the characteristics of a whole population of neutrons, by selecting random groups of individuals. Through new research published in EPJ Plus, a team led by Henri Hutinet at the French Alternative Energies and Atomic Energy Commission implement and validate eTLE’s reliability for the first time.