CoE RAISE’s Post

🚀 Advancements in Physics-Informed Machine Learning for Surrogate Models are revolutionizing the future of Computational Fluid Dynamics (CFD)! NVIDIA Modulus is empowering engineers with an open-source framework to build, train, and fine-tune Physics-ML models through a simple Python interface. It enables the construction of AI surrogate models that merge physics-driven causality with simulation and observed data, facilitating real-time predictions. With generative AI and diffusion models, Modulus enhances engineering simulations and generates higher-fidelity data for scalable, responsive designs. It supports creating large-scale digital twin models across various physics domains, from CFD and structural mechanics to electromagnetics. 🔧 Key Features: - PINNs Integration: Leverage Physics-Informed Neural Networks (PINNs) to solve complex physical problems with precision and speed. PINNs combine the power of machine learning with physical laws, enabling highly accurate simulations and predictions. - Machine Learning Capabilities: Utilize state-of-the-art machine learning techniques to create surrogate models, drastically reducing computation time while maintaining high fidelity. - Scalability: Effortlessly scale your computations from a single GPU to large-scale HPC clusters. - User-Friendly Interface: Intuitive and easy to use, even for those new to HPC. Discover how Modulus can revolutionize your development process. [Learn more about NVIDIA Modulus](https://lnkd.in/eADA8kMC) #HPC #NVIDIA #Modulus #PINNs #MachineLearning #TechInnovation #DeveloperTools

𝐀𝐈 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫𝐬 𝐯𝐬. 𝐍𝐞𝐮𝐫𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬 By April 2024, Western Sydney University is set to revolutionize the world of computing with the introduction of 𝐃𝐞𝐞𝐩𝐒𝐨𝐮𝐭𝐡, a high-performance computing (HPC) cluster provided by the International Centre for Neuromorphic Systems. This breakthrough represents a pivotal moment in the evolution of computing, juxtaposing traditional AI accelerators with the burgeoning field of 𝐧𝐞𝐮𝐫𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬. 💡 𝐖𝐡𝐚𝐭 𝐌𝐚𝐤𝐞𝐬 𝐃𝐞𝐞𝐩𝐒𝐨𝐮𝐭𝐡 𝐔𝐧𝐢𝐪𝐮𝐞? DeepSouth is 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐞𝐝 𝐭𝐨 𝐦𝐢𝐦𝐢𝐜 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐛𝐫𝐚𝐢𝐧, capable of a staggering 228 trillion 𝐬𝐲𝐧𝐚𝐩𝐭𝐢𝐜 operations per second (NOT FLOPS OR TOPS 😮) . Unlike traditional supercomputers that rely on brute-force power, 𝐃𝐞𝐞𝐩𝐒𝐨𝐮𝐭𝐡 𝐮𝐭𝐢𝐥𝐢𝐳𝐞𝐬 𝐬𝐩𝐢𝐤𝐢𝐧𝐠 𝐧𝐞𝐮𝐫𝐨𝐧𝐬, an approach that's not only power-efficient but also more effective in solving complex problems. 🔍 𝐀𝐈 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫𝐬 𝐯𝐬. 𝐍𝐞𝐮𝐫𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬: 𝐀 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐒𝐡𝐢𝐟𝐭 Traditional AI accelerators, such as GPUs and CPUs, have been the backbone of high-speed computing and AI tasks. However, as Prof. Andre van Schaik of ICNS notes, these systems are "𝐣𝐮𝐬𝐭 𝐭𝐨𝐨 𝐬𝐥𝐨𝐰" for certain simulations, not to mention their substantial power requirements. Neuromorphic computing, on the other hand, offers a fascinating alternative, mimicking neural networks to deliver 𝐠𝐫𝐞𝐚𝐭𝐞𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐥𝐨𝐰𝐞𝐫 𝐩𝐨𝐰𝐞𝐫 𝐜𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧. 🧠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: 𝐀 𝐆𝐚𝐭𝐞𝐰𝐚𝐲 𝐭𝐨 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐫𝐚𝐢𝐧 DeepSouth is more than a supercomputer; it's a gateway to a deeper understanding of the brain, with applications in sensing, biomedical research, robotics, space exploration, and large-scale AI. The rise of neuromorphic computing marks a pivotal shift in our approach to solving complex problems. 𝐈𝐭'𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐫𝐚𝐰 𝐩𝐨𝐰𝐞𝐫; 𝐢𝐭'𝐬 𝐚𝐛𝐨𝐮𝐭 𝐬𝐦𝐚𝐫𝐭, 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭, 𝐚𝐧𝐝 𝐚𝐝𝐚𝐩𝐭𝐚𝐛𝐥𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 that mirrors the most sophisticated system known to us – 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐛𝐫𝐚𝐢𝐧. #AI #DeepSouth #Innovation #FutureOfComputing #WesternSydneyUniversity #NeuromorphicComputing #hyperautomation

📰 News: 📰 🌟 Exploring the Power of Quantum Computing and AI! 🌟 Did you know that the combination of quantum computing and artificial intelligence -AI- has the potential to revolutionize industries like computational chemistry? SandboxAQ, a quantum and AI technology company, is blazing a trail in this exciting field, partnering with GPU giant Nvidia to push the boundaries of computational chemistry simulations. 🚀 Here are some remarkable achievements from their collaboration: 🔬 Leveraged the power of Large Quantitative Models -LQMs- and Nvidia's CUDA-accelerated Density Matrix Renormalization Group -DMRG- algorithm to perform highly accurate Quantitative AI simulations. 🔬 Achieved more than 80x speed-up compared to CPU-based calculations. 🔬 Doubled the sizes of computable catalysts and enzyme active sites calculated by the system. This breakthrough milestone has opened up new possibilities in drug discovery, disease treatment, and material science. SandboxAQ and Nvidia are just getting started, with plans to scale up to larger GPU clusters and expand beyond energetics calculations. 📈 What do you think about the potential of quantum computing and AI for your business? Share your thoughts below and let's dive into the quantum frontier together! 💭✨ 🔗 Read the full article here: -Link to the article- Don't miss out on future updates and insights about quantum computing and AI! Make sure to follow Spin Quantum Tech and join the conversation. 🚀💡 -QuantumComputing -AI -Innovation -FutureTech -ComputationalChemistry -SpinQuantumTech -TransformingTheFuture -CTA #Future #Futurism #Innovation #Business #ArtificialIntelligence #QuantumComputing #SQT https://lnkd.in/eP4TRSTU

The Institute of Science and Technology Austria (ISTA) has announced a significant investment in a cutting-edge computing cluster, featuring over 100 NVIDIA H100 Tensor Core GPUs. This upgrade is aimed at enhancing ISTA’s computing infrastructure, particularly for scaling up machine learning and training large language models in the field of generative AI. The multi-million dollar investment underscores ISTA's commitment to advancing AI research in the public sphere, positioning the institute as a key player in European computational research. This state-of-the-art GPU cluster will allow ISTA researchers to accelerate AI research at a scale comparable to private sector capabilities, which have predominantly driven recent advances in the field. By focusing on AI and machine learning, ISTA hopes to contribute to groundbreaking developments in academic research, further solidifying its role as a European hub for AI innovation and computational science. The investment also reflects a broader trend of academic institutions investing heavily in high-performance computing to keep pace with the rapid advancements in AI technology, ensuring that public research remains competitive in this fast-evolving sector.

🚀 AI Physics Powered by NVIDIA on the Rescale platform is revolutionizing science and engineering. 🌟 Learn more about this game-changer: https://bit.ly/3VBKQEz Traditional simulations meet cutting-edge AI algorithms, creating a powerhouse for innovation. Here’s why this matters: • Speed: Simulations run 1000X+ faster, slashing design optimization time from weeks to hours. • Accuracy: Achieve over 98% accuracy compared to traditional methods. • NVIDIA Tech: Leverage NVIDIA’s AI Enterprise suite for pre-trained models and GPU-accelerated cloud infrastructure. • AI Marketplace: Explore AI Physics solvers like NAVASTO, Neural Concept, and more. • Seamless Integration: Easily integrate with existing workflows. #AI #Engineering #Innovation #NVIDIA #Science #Simulation

I’m excited to share a chapter from my report on the Photovoltaic Project at Syddansk Universitet - University of Southern Denmark. In this chapter, I thoroughly examine the integration of edge computing with the SeeedStudio J4012 recomputer, focusing on its architecture and design to implement practical actions using deep learning technologies. This includes leveraging NVIDIA Jetson's capabilities for real-time inference on large photovoltaic (PV) installations, where speed and accuracy are crucial. I detail the deployment process from scratch, starting with flashing the Jetson device, installing necessary libraries, and utilizing cutting-edge solutions offered by Seeed Studio on their EdgeAI device. The chapter provides an in-depth look at configuring the Jetson platform, exploring its specifications, and understanding various deep learning capabilities, including advanced video processing techniques. I also address the importance of switching between different deep learning frameworks and efficiently utilizing DLA (Deep Learning Accelerator) and DeepStream for optimized performance. Practical commands and configurations are included to streamline the integration of camera connections and server setups, aiming for seamless real-time inference.In summary, I tried to ensure  a thorough understanding of implementing advanced deep learning solutions on Jetson device, drawing on insights from various NVIDIA sources and blogs. #SyddanskUniversitet #EdgeComputing #DeepLearning #NVIDIAJetson #SeeedStudio #RealTimeInference #Photovoltaic #AI #MachineLearning #NVIDIAAI #VideoProcessing #Ultralytics #TechInnovation

Have you heard the buzz about #AIPhysics? AI Physics enhances traditional simulation methods with specialized #machinelearning algorithms to dramatically accelerate #engineering and #scientific discovery. Companies using this new technology are seeing unprecedented results, including: 10,000x+ Accelerated Results for #DesignEvaluation 99%+ Accuracy Compared to Traditional #CFD #Simulation 85% Improvement in #Computing Resource Efficiency Rescale’s end-to-end AI-physics platform, powered by NVIDIA, facilitates multi-physics simulation #datageneration, #AI model training, running inference, and model validation in one place, making it a game-changer for #computationalengineering, #science, and #R&D teams. General Motors is already leveraging this exciting new technology on Rescale for it's Motorsports Division, giving them a competitive edge on the track! https://lnkd.in/e472K5MA 

2️⃣ Day 2/10 for hands-on AI/ML in engineering simulation! 🤓 In alignment with my recent book publication (https://geni.us/JaXp), I will be posting about 10 different AI/ML projects in our world of CAE/CFD/FEA/etc. 📆 Today, we have: TURBULENCE SUPER RESOLUTION 🤖 I love this topic. The industrial applications are obvious - - simulate with a coarse mesh resolution and 'upscale' the resolution significantly with machine learning as a means for 'correcting' (improving) the results. While everything has limitations, the possible impact to industry timelines and fidelity within reach is exciting! This example I did used direct numerical simulation data of homogenous isotropic turbulence fields from The Johns Hopkins University Turbulence Database. In case you haven't seen it, it's a great resource for machine learning projects. In this video I am showing a sample of the results from snapshots in time for a cube with 2M points. It's just a sample of the results from a fun weekend project, so need to overly scrutinize the plotting as if it's in a peer reviewed publication ;) I did this with NVIDIA Modulus #machinelearning #simulation #cfd #physics #ai #nvidia

"The potential for sparse coding is huge because it is energy efficient." - Bob Beachler, our VP of Product, in a new Semiconductor Engineering article on the future of #neuromorphic computing. Neuromorphic engineering aims to create brain-inspired AI hardware that leverages sparse coding to minimize energy consumption. By using "minuscule amounts of energy to represent spikes," neuromorphic systems could significantly boost efficiency compared to traditional approaches. As the demand for low-power, high-performance AI grows, neuromorphic computing is moving closer to market reality. We at Untether AI are proud to be at the forefront of this exciting field! Read the full article by Karen Heyman to learn more: https://lnkd.in/gaem8MTV.

Just look at the potential accelerations in planning tasks… 10x to 90x range. Project Management professionals need to embrace Generative AI or be outclassed by those who do. I see so much resistance in my industry, although an issue not yet fully resolved is about the safeguarding of confidential data.