Leverhulme Research Centre for Functional Materials Design’s Post

Excited to share the topic of my upcoming master’s thesis at IGMR - RWTH Aachen University: "Automated Dismantling of Screws by Robots: Development of a System for Detecting and Loosening Screw Connections for Flexible Dismantling of Electronic Components." The system will utilize the deep neural network framework YOLO, trained specifically to detect screws in electronic components. End of life electronic components are very different, which is why a flexible approach to detect screws is required. 🤖

– My research could potentially help mapping the seafloor more efficiently and with higher resolution which would help scientists understand the ocean better, says Yiping Xie who is defending his doctoral thesis today at 14:00. Yiping Xie, WASP PhD student at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology, is defending the doctoral thesis “Bathymetric Surveying Through Neural Inverse Sonar Modeling” today, June 5th, at 14:00. Join the public defense online or in E3, Osquars backe 14, Stockholm. More information and link to thesis 👉 https://lnkd.in/dGEGbfpY What are the main findings of your doctoral thesis? – Inspired by 3D reconstruction from camera images, we can use similar deep learning based approaches to reconstruct bathymetry from imaging sonar data. In what way can your research be of importance to our society in the future? – My research could potentially help mapping the seafloor more efficiently and with higher resolution which would help scientists understand the ocean better. Main supervisor: John Folkesson #wasp #waspgraduateschool #phd

Machine learning + Materials development 🚀 It was an exciting week at Ljubljana! Autonomous laboratories, material discovery, and manufacturing optimization. It is hard to describe every amazing thing I saw at @Machine Learning Modalities for Materials Science (ML4MS 2024)! 💎 During the conference, I had an amazing opportunity to present my work on the atomic-scale simulations of #alloys. Specifically, my research covers metallic glasses – both challenging and application-promising materials. I am excited that I can leverage the power of digital tools💻to boost materials development 📈! I could not achieve those results without the support of the team from NOMATEN (Silvia Bonfanti, Anshul D. S. Parmar, and Mikko Alava), and Warsaw University of Technology (Jan S. Wróbel). Thanks to Jozef Stefan Institute for hosting such an amazing event, and DAEMON COST for providing me the opportunity to make this trip 🌍. I am looking forward to more such amazing events ✨ #MachineLearning #MaterialsScience #AI #RnD #Research #Innovation #MetallicGlasses

This is a very comprehensive review/concept paper that highlights state-of-the-art and challenges of #neuromorphic and #memorymatter active #hydrogel systems. Great work led by Jiongyi Yan, with myself, James Armstrong and Adam Perriman on the team. We thank the support of the European Research Council (ERC) and #UKRI. The paper is #openaccess #biobased #metamaterials

"Visualize all the forces applied to the droplet and its velocity." ( Watch 2:45 ) No this is not a video generation model but a generative model powered by real physics. Imagine you teach a robot how to use something like unreal engine. "Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications." And it's open source, guess I will be on my desk trying this out all day today to test if this is as good as it is claimed... ( Source: Zhou Xian )

A Camera beyond Imagination 📸 MIT researchers developed an extraordinary camera capable of capturing 1 trillion frames per second, fast enough to record the movement of light. This means it can film events that happen at speeds too quick for the human eye to perceive, like the way light travels through objects. The camera uses a technique called "streak camera imaging," allowing scientists to visualize and study ultrafast phenomena. This breakthrough has opened up new possibilities in fields like physics, biology, and engineering, where understanding such rapid processes can lead to innovations in science and technology. #mit #sciencebreakthrough #scientificdiscovery Note:  1. This Post is Offcourse Automated (Not Posted for any like,repost or Comments). 2. Inta Supports the Multi video functions on post but linkdin not That's why You may See the Image insted of video You can visit here -> https://lnkd.in/dsApupgZ To See Full Post Which is taken from https://lnkd.in/dJW9BYr6    

These days, our student of UCU's Master's program in Data Science, Olena Ivina, and her research partner from the University of Toronto, Brokoslaw Laschowski, are presenting their joint research at the International Conference on Aging, Innovation, and Rehabilitation (ICAIR) in Toronto. The conference focuses on advancing rehabilitation technologies to enhance the quality of life for individuals with diverse diseases and disabilities. The goal of this research is to find the optimal combination of signal processing, feature extraction, and machine learning for EEG neural decoding of leg movements to control robotic exoskeletons for locomotor assistance and rehabilitation. To read the article: https://lnkd.in/dDtZX8cY

Physics simulators written from first-principles are not perfect, but don't throw out the baby with the bath water! You're almost never going to develop or code 100% accurate models of the real world for new types of phenomena, and when you do, you're going to spend a ton of time to do so. On the other hand, if you use ML to learn physics model-free, you're throwing out a lot of useful information, and you're going to need a ton of data. We show that even in partially-observable physical systems such as soft robots, relatively small datasets can augment base simulators with residual physics and provide superior accuracy in dynamical settings, and even faster simulation runtimes. We're excited to push this approach to more challenging problems in soft robot modeling in the near future! https://lnkd.in/gsjajMcy Joint work with Junpeng Gao, Mike Yan Michelis, and Robert Katzschmann.

NeurIPS in Vancouver is Approaching! Congratulations to our Linz-based researchers from the Institute of Machine Learning at Johannes Kepler Universität Linz and NXAI! 🎓 We are proud to announce that two papers from Linz have been accepted to NeurIPS! 🙌 1️⃣ xLSTM: Extended Long Short-Term Memory 2️⃣ Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators This is our first time attending NeurIPS, and we’re excited to bring our entire research team along. We can't wait to exchange ideas with all of you! 👉 What must we not miss at NeurIPS? 👉 What are your favorite papers this year? 🔗 Links to the papers are in the comments below. Sepp Hochreiter, Johannes Brandstetter, Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra P., Michael Kopp, Günter Klambauer, Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner #NeurIPS2024 #AIResearch #xLSTM #PhysicsTransformers #MachineLearning #NXAI #JKULinz #AIInnovation #TechCommunity

This Thursday 🚀 16:00H (Berlin time) #FAUMoDLecture at FAU Erlangen-Nürnberg: Thu. October 24, 2024 📌 On-site / Online "New avenues for the interaction of computational mechanics and machine learning" 🔹Prof. Dr. Paolo Zunino, Politecnico di Milano "Neural networks and learning algorithms have gained substantial attention among researchers engaged in computational mechanics (...) After reviewing the main trends in this field, we will discuss novel emerging approaches such as the application of learning algorithms to expedite the resolution of linear systems or to foster the approximation of multiscale problems." Find the zoom link & more details: https://lnkd.in/dKAeth7T #FAU #FAUMoD #NN #neuralNetworks #hybrid #lecture #computationalMechanics #machineLearning #lectureSeries #Erlangen #Bavaria #Germany #ML