There��s never enough time to do everything, even in engineering education. Employers want engineers capable of wielding simulation tools to expedite iterative research, design, and development. Some instructors try to address this by teaching for weeks or months, on derivations of numerical methods, approaches to discretization, the intricacies of turbulence models, and more. Unfortunately��
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As the world faces the urgent need to combat climate change, carbon capture and storage (CCS) has emerged as a crucial technology for achieving net-zero emissions. The CCS technology��which involves capturing carbon dioxide (CO2), either from industrial emissions or through direct air capture (DAC), and securely storing it in the subsurface��can drive much-needed decarbonization strategies and help��
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NVIDIA PhysicsNeMo 24.07 brings new GNN enhancements and application samples for training with large meshes.
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Despite the continuous improvement of weather forecasts over the last few decades, uncertainties due to meteorological measurements and models mean that ensemble forecasts remain critical to weather forecasting. Ensemble forecasts estimate this uncertainty by running multiple simulations over the same forecast horizon. Comparing the different outcomes then paints a more detailed picture of the��
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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��
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PhysicsNeMo v24.04 delivers an optimized CorrDiff model and Earth2Studio for exploring weather AI models.
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NVIDIA PhysicsNeMo 24.01 updates distributed utilities and samples for physics informing DeepONet and GNNs.
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Now available, NVIDIA PhysicsNeMo 23.11 introduces a diffusion modeling framework and novel architectures.
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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��
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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��
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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��
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NVIDIA PhysicsNeMo 23.09 is now available, providing ease-of-use updates, fixes, and other enhancements.
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NVIDIA PhysicsNeMo is now part of the NVIDIA AI Enterprise suite, supporting PyTorch 2.0, CUDA 12, and new samples.
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Robotics simulation enables virtual training and programming that can use physics-based digital representations of environments, robots, machines, objects, and other assets.
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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��
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This version 23.05 update to the NVIDIA PhysicsNeMo platform expands support for physics-ML and provides minor updates.
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CO2 capture and storage technologies (CCS) catch CO2 from its production source, compress it, transport it through pipelines or by ships, and store it underground. CCS enables industries to massively reduce their CO2 emissions and is a powerful tool to help industrial manufacturers achieve net-zero goals. In many heavy industrial processes, greenhouse gas (GHG) emissions cannot be avoided in the��
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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��
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