RP Photonics’ Post

Within a quantitative model for the fiber amplifier or fiber laser, we somehow need to describe the light propagating in the fiber. How that should be done in detail, depends very much on the circumstances. In most cases, we are dealing with different light waves at substantially different wavelengths – for example, the pump wave and a signal wave. In more complicated cases, we may have multiple pump and signal waves, and there may also be light from amplified spontaneous emission (ASE). https://lnkd.in/dyC2ywQE #photonics #simulation #Learning

Really interesting! ‘Our innovative workflow autonomously, comprehensively and locally characterises the crystallographic information and 3D orientation of the crystal phases, the elemental composition, and the strain maps of devices from (scanning) transmission electron microscopy data. It converts a manual characterisation process that traditionally takes days into an automatic routine completed in minutes. This is achieved through a physics-guided artificial intelligence model that combines unsupervised and supervised machine learning in a modular way to provide a representative 3D description of the devices, materials structures, or samples under analysis. To culminate the process, we integrate the extracted knowledge to automate the generation of both 3D finite element and atomic models of millions of atoms acting as digital twins, enabling simulations that yield essential physical and chemical insights crucial for understanding the device's behaviour in practical applications.’

🔎 Our new blog post explores multi-scale #CFD simulations, where models and computational techniques are integrated to capture detailed physics at microscales while efficiently predicting flow behavior at macroscales. 🔬 🧩 It highlights challenges such as computational complexity, model coupling, and data transfer, and suggests solutions like hierarchical modeling, Adaptive Mesh Refinement (AMR), and coupled solver technologies. Read more 👇

Discover Linien, our open-source tool for easy frequency stabilization on the Red Pitaya STEMlab 125-14. Dive into our latest blog post to see why the frequency stabilization of lasers is one of the most important parts of many atomic, molecular and optical (AMO) physics experiments. 🌐 #redpitayablog #linien #physicsexperiments

For who is interested in deep understanding between target Radar returns and the EM scattering: Measurements-Based Radar Signature Modeling, MIT Lincoln Laboratory Series https://lnkd.in/dhhZQ583

We are happy to announce that our article with Davide Torlo, Monica Nonino and Gianluigi Rozza "An optimisation–based domain–decomposition reduced order model for parameter-dependent non–stationary fluid dynamics problems" has been published in Computers & Mathematics with Applications (Elsevier). This work studies parametric non-stationary fluid dynamics problems within model order reduction and optimisation-based domain decomposition settings. In particular, we conduct a rigorous analysis of the optimal control problem resulting from the domain-decomposition formulation and provide effective iterative algorithms to solve the high-fidelity model as well as construct local reduced basis spaces. On the reduced level we compare different model order reduction techniques: POD–Galerkin and non–intrusive neural network (POD-NN) procedures. We show that the classical POD–Galerkin is more robust and accurate also in transient areas, while the neural network can obtain simulations very quickly though being less precise in the presence of discontinuities in time or parameter domain. You may find the article here: https://lnkd.in/dGk8ib62 Many thanks to my group and my collaborators. SISSA mathLab

How to scale Simulations with NXAI's NeuralDEM’s Multi-Branch Neural Operators? The real breakthrough in NeuralDEM is its multi-branch neural operator architecture, which handles complex interactions across large systems efficiently. Here’s how it works: • 𝗠𝗮𝗶𝗻-𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀: Model core physics (e.g., particle displacement and fluid dynamics). • 𝗢𝗳𝗳-𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀: Directly simulate macroscopic properties like mixing or transport, bypassing the need for particle-by-particle accuracy. This design enables NeuralDEM to simulate: 🔹 Hoppers with varying particle friction and slope angles, predicting outflow rates and drainage times. 🔹 Fluidized bed reactors, coupling 500,000 particles with 160,000 CFD cells in transient regimes. Unlike DEM, NeuralDEM generalizes across unseen parameter combinations and provides physically accurate results over long time horizons. Find out more: https://lnkd.in/dPx9gsxP #MachineLearning #NeuralOperators #SimulationScalability #AIInnovation #IndustrialSimulations #GranularMaterials #AI

This weekend I developed my (relatively simple) electromagnetic wave solver in Python with the FDTD-method. In my opinion, this project was (maybe a lot) easier to accomplish than writing a CFD-code from scratch. I based myself on the book: A. Taflove, Computational Electrodynamics: The Finite-Difference Time-Domain Method, (Artech House, Norwood, MA, 1995). Some testcases were developed where a bunch of objects with varying conductivity, relative permittivity and permeability are hit by electromagnetic waves. Anyone interested, can watch them here: https://lnkd.in/eVfa4TPn Computational electromagnetics (CEM), computational electrodynamics or electromagnetic modeling is the process of modeling the interaction of electromagnetic fields with physical objects and the environment using computers. The finite-difference time-domain (FDTD) method is a widespread numerical tool for full-wave analysis of electromagnetic fields in complex media and for detailed geometries. Applications of the FDTD method cover a range of time and spatial scales, extending from subatomic to galactic lengths and from classical to quantum physics. Technology areas that benefit from the FDTD method include biomedicine — bioimaging, biophotonics, bioelectronics and biosensors; geophysics — remote sensing, communications, space weather hazards and geolocation; metamaterials — sub-wavelength focusing lenses, electromagnetic cloaks and continuously scanning leaky-wave antennas; optics — diffractive optical elements, photonic bandgap structures, photonic crystal waveguides and ring-resonator devices; plasmonics — plasmonic waveguides and antennas; and quantum applications — quantum devices and quantum radar. #simulation #physics #computational #electromagnetics #maxwell #solver #numerical #cem #fdtd #dynamic #infrared #thermal #wave

Today I gladly share with my network that our paper entitled "A data-driven turbulence modeling for the Reynolds stress tensor transport equation" has been published at the International Journal for Numerical Methods in Fluids! It is comprised by a significant part of my master's thesis. I co-authored it with Matheus Altomare, MSc., Bernardo Brener and my thesis advisor Roney Thompson. In this work we have introduced a modified transport equation for the Reynolds stress that is driven by a source term predicted by neural networks. The transport equation was coupled with the momentum balance and the SIMPLE algorithm for pressure, forming a full data-driven Reynolds stress model, which was used to correct RANS simulations. DNS simulations for the square-duct flow were used to train the newtork and validate the results. You can access it at https://lnkd.in/dJaB76Ny and it is fully available for free at https://lnkd.in/dZTyKt8H You can also access the model's implementation as an OpenFOAM turbulence model at this repository on Github https://lnkd.in/dpb9UqRm