Flooding poses a significant threat to 1.5 billion people, making it the most common cause of major natural disasters. Floods cause up to $25 billion in global economic damage every year. Flood forecasting is a critical tool in disaster preparedness and risk mitigation. Numerical methods have long been developed that provide accurate simulations of river basins. With these, engineers such as those…
]]>NVIDIA PhysicsNeMo 24.07 brings new GNN enhancements and application samples for training with large meshes.
]]>An open ecosystem for physics-informed machine learning (physics-ML) fosters innovation and AI engineering applications. Physics-ML embeds into the learning process the knowledge of physical laws that govern a given dataset. This enables scientists to use prior knowledge to help train a neural network, making it more generalizable and efficient. Yet, as physics-ML is a growing field of…
]]>The world’s energy system is increasingly complex and distributed due to increasing renewable energy generation, decentralization of energy resources, and decarbonization of heavy industries. Energy producers are challenged to optimize operational efficiency and costs within hybrid power plants generating both renewable and carbon-based electricity. Grid operators have less time to dispatch energy…
]]>PhysicsNeMo v24.04 delivers an optimized CorrDiff model and Earth2Studio for exploring weather AI models.
]]>NVIDIA PhysicsNeMo 24.01 updates distributed utilities and samples for physics informing DeepONet and GNNs.
]]>Now available, NVIDIA PhysicsNeMo 23.11 introduces a diffusion modeling framework and novel architectures.
]]>AI is quickly becoming an integral part of diverse industries, from transportation and healthcare to manufacturing and finance. AI powers chatbots, recommender systems, computer vision applications, fraud prevention, and autonomous vehicles. It also has broad applications in engineering and science. Physics-informed machine learning (physics-ML) leverages knowledge of the physical world to…
]]>A Stanford University team is transforming heart healthcare with near real-time cardiovascular simulations driven by the power of AI. Harnessing physics-informed machine learning surrogate models, the researchers are generating accurate and patient-specific blood flow visualizations for a non-invasive window into cardiac studies. The technology has far-reaching scope…
]]>Simulations are quintessential for complex engineering challenges, like designing nuclear fusion reactors, optimizing wind farms, developing carbon capture and storage techniques, or building hydrogen batteries. Designing such systems often requires many iterations of scientific simulations that are computationally expensive to run. Solvers and parameters must often be tuned individually to each…
]]>NVIDIA Jetson Orin is the best-in-class embedded platform for AI workloads. One of the key components of the Orin platform is the second-generation Deep Learning Accelerator (DLA), the dedicated deep learning inference engine that offers one-third of the AI compute on the AGX Orin platforms. This post is a deep technical dive into how embedded developers working with Orin platforms can…
]]>NVIDIA Jetson Orin is the best-in-class embedded AI platform. The Jetson Orin SoC module has the NVIDIA Ampere architecture GPU at its core but there is a lot more compute on the SoC: The NVIDIA Orin SoC is powerful, with 275 peak AI TOPs, making it the best embedded and automotive AI platform. Did you know that almost 40% of these AI TOPs come from the two DLAs on NVIDIA Orin?
]]>NVIDIA PhysicsNeMo is now part of the NVIDIA AI Enterprise suite, supporting PyTorch 2.0, CUDA 12, and new samples.
]]>NVIDIA PhysicsNeMo is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine learning (physics-ML) models. PhysicsNeMo is available as OSS (Apache 2.0 license) to support the growing physics-ML community. The latest PhysicsNeMo software update, version 23.05, brings together new capabilities…
]]>Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to…
]]>The latest version of NVIDIA PhysicsNeMo, an AI framework that enables users to create customizable training pipelines for digital twins, climate models, and physics-based modeling and simulation, is now available for download. This release of the physics-ML framework, NVIDIA PhysicsNeMo v22.09, includes key enhancements to increase composition flexibility for neural operator architectures…
]]>NVIDIA PhysicsNeMo is a physics-machine learning platform that blends the power of physics with data to build high-fidelity, parameterized AI surrogate models that serve as digital twins to simulate with near real-time latency. This cutting-edge framework is expanding its interactive simulation capabilities by integrating with the NVIDIA Omniverse (OV) platform for real-time virtual-world…
]]>NVIDIA is now publishing Linux GPU kernel modules as open source with dual GPL/MIT license, starting with the R515 driver release. You can find the source code for these kernel modules in the NVIDIA/open-gpu-kernel-modules GitHub page This release is a significant step toward improving the experience of using NVIDIA GPUs in Linux, for tighter integration with the OS, and for developers to…
]]>NVIDIA announces the newest release of the CUDA development environment, CUDA 11.4. This release includes GPU-accelerated libraries, debugging and optimization tools, programming language enhancements, and a runtime library to build and deploy your application on GPUs across the major CPU architectures: x86, Arm, and POWER. CUDA 11.4 is focused on enhancing the programming model and…
]]>CUDA is the software development platform for building GPU-accelerated applications, providing all the components you need to develop applications that use NVIDIA GPUs. CUDA is ideal for diverse workloads from high performance computing, data science analytics, and AI applications. The latest release, CUDA 11.3, and its features are focused on enhancing the programming model and performance of…
]]>CUDA is the software development platform for building GPU-accelerated applications, providing all the components needed to develop applications targeting every NVIDIA GPU platform for general purpose compute acceleration. The latest CUDA release, CUDA 11.2, is focused on improving the user experience and application performance for CUDA developers. CUDA 11.2…
]]>This blog discusses how an application developer can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano Developer Kit with MATLAB. In previous posts, we explored how you can design and train deep learning networks in MATLAB and how you can generate optimized CUDA code from your deep learning algorithms. In our experience working with deep learning engineers…
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