NVIDIA Warp, a simulation computing framework, is now accessible to all developers.
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NVIDIA cuDSS is a first-generation sparse direct solver library designed to accelerate engineering and scientific computing. cuDSS is increasingly adopted in data centers and other environments and supports single-GPU, multi-GPU and multi-node (MGMN) configurations. cuDSS has become a key tool for accelerating computer-aided engineering (CAE) workflows and scientific computations across��
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Computational fluid dynamics (CFD) is used in industry and academia to address a wide range of use cases, including external aerodynamics, internal flows, heat transfer, combustion, reacting flows, free-surface flows, multiphase flows, and aero-acoustics. CFD solvers are commonly used to simulate external aerodynamics, which helps reduce drag and improve fuel efficiency��
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With the latest release of Warp 1.5.0, developers now have access to new tile-based programming primitives in Python. Leveraging cuBLASDx and cuFFTDx, these new tools provide developers with efficient matrix multiplication and Fourier transforms in Python kernels for accelerated simulation and scientific computing. In this blog post, we��ll introduce these new features and show how they can be used��
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The new release includes new network architectures for external aerodynamics application as well as for climate and weather prediction.
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Accelerated computing is enabling giant leaps in performance and energy efficiency compared to traditional CPU computing. Delivering these advancements requires full-stack innovation at data-center scale, spanning chips, systems, networking, software, and algorithms. Choosing the right architecture for the right workload with the best energy efficiency is critical to maximizing the performance and��
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Everything that is manufactured is first simulated with advanced physics solvers. Real-time digital twins (RTDTs) are the cutting edge of computer-aided engineering (CAE) simulation, because they enable immediate feedback in the engineering design loop. They empower engineers to innovate freely and rapidly explore new designs by experiencing in real time the effects of any change in the simulation.
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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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Simulations play a critical role in advancing science and engineering, especially in the vast field of fluid dynamics. However, high-fidelity fluid simulations require extensive computational resources, often constraining practical applications. Accurately simulating complex flows can take weeks of computational effort, slowing down advancements in critical fields such as aerospace and��
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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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Engineering simulation is used across industries to accelerate product development. Simulations are used to check the safety of aircraft, cars, and buildings, ensure that your mobile phone has a signal wherever you go, and maximize the range of the newest electric vehicles. It reduces the need for expensive, time-consuming physical testing and enables engineers to iteratively improve designs much��
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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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The world of computing is on the precipice of a seismic shift. The demand for computing power, particularly in high-performance computing (HPC), is growing year over year, which in turn means so too is energy consumption. However, the underlying issue is, of course, that energy is a resource with limitations. So, the world is faced with the question of how we can best shift our computational��
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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 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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According to the American Society of Quality (ASQ), defects cost manufacturers nearly 20% of overall sales revenue. The products that we interact with on a daily basis��like phones, cars, televisions, and computers��must be manufactured with precision so that they can deliver value in varying conditions and scenarios. AI-based computer vision applications are helping to catch defects in the��
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Most modern digital chips integrate large numbers of macros in the form of memory blocks or analog blocks, like clock generators. These macros are often much larger than standard cells, which are the fundamental building blocks of digital designs. Macro placement has a tremendous impact on the landscape of the chip, directly affecting many design metrics, such as area and power consumption.
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The latest version of the NVIDIA PhysX 5 SDK is now available under the same open source license terms as NVIDIA PhysX 4 to help expand simulation workflows and applications across global industries. You can find this much-anticipated update on the NVIDIA-Omniverse/PhysX GitHub repository. A longtime GameWorks technology, PhysX has become the primary physics engine and a key foundational��
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Learn the basics of physics-informed deep learning and how to use NVIDIA PhysicsNeMo, the physics machine learning platform, in this self-paced online course.
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Accelerate your AI-based simulations using NVIDIA PhysicsNeMo. The 22.07 release brings advancements with weather modeling, novel network architectures, geometry modeling, and more��plus performance improvements.
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When a technology reaches the required level of maturity, adoption transitions from those considered visionaries to early majority adopters. Now is such a critical and transitional moment for the largest single segment of industrial high-performance computing (HPC). The end of 2021 and beginning of 2022 saw the two largest commercial computational fluid dynamics (CFD) tool vendors��
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From physics-informed neural networks (PINNs) to neural operators, developers have long sought after the ability to build real-time digital twins with true-to-form rendering, robust visualizations, and synchronization with the physical system in the real world by streaming live sensor data. The latest release of PhysicsNeMo brings us closer to this reality. PhysicsNeMo 22.03��
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Today NVIDIA announced the availability of NVIDIA PhysicsNeMo (previously known as SimNet), a platform to train neural networks using governing physics equations along with observed or simulated data. The robust and high-fidelity models produced by the PhysicsNeMo framework enable the acceleration of design exploration for multiphysics systems�Cideal for digital twin development.
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NVIDIA PhysicsNeMo was previously known as NVIDIA SimNet. Today, NVIDIA announces the release of PhysicsNeMo v21.06 for general availability, enabling physics simulations across a variety of use cases. NVIDIA PhysicsNeMo is a Physics-Informed Neural Networks (PINNs) toolkit for engineers, scientists, students, and researchers who either want to get started with AI-driven physics��
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A container is a portable unit of software that combines the application and all its dependencies into a single package that is agnostic to the underlying host OS. In a high-performance computing (HPC) environment, containers remove the need for building complex environments or maintaining environment modules, making it easy for researchers and systems administrators to deploy their HPC��
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Industrial-Scale AI content is at GTC. From April 12-16, 1,400 live and on-demand sessions will be at your fingertips. Many topics will be covered including solutions in computational fluid dynamics, predictive maintenance, inspection, and factory logistics across Industrial Manufacturing, Aerospace, Oil and Gas, Electronic Design Automation (EDA), Engineering Simulation (CAE), and more.
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From weather forecasting and energy exploration, to computational chemistry and molecular dynamics, NVIDIA compute and networking technologies are optimizing nearly 2,000 applications across a broad-range of scientific domains and industries. By leveraging GPU-powered parallel processing, users can accelerate advanced, large-scale applications efficiently and reliably, paving the way to scientific��
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NVIDIA PhysicsNeMo was previously known as NVIDIA SimNet. Simulations are prevalent in science and engineering fields and have been recently advanced by physics-driven AI. Join this webinar to learn how NVIDIA PhysicsNeMo addresses a wide range of use cases involving coupled forward simulations without any training data, as well as inverse and data assimilation problems.
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Recently at the International Conference on Machine Learning (ICML), researchers from Princeton University and Columbia University introduced SketchGraphs, a large-scale dataset of computer-aided design (CAD) sketches that can be used to train models for AI-aided design. Machine learning CAD models trained using SketchGraphs have the potential to enable more efficient design processes for��
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NVIDIA PhysicsNeMo was previously known as NVIDIA SimNet. A new demo introduces the recently announced NVIDIA PhysicsNeMo Toolkit, the first multiphysics (CFD and Heat Transfer) analysis using physics-informed neural networks (PINNs). Simulations form an integral part of product design to reduce significant iterations in physical prototyping and testing to improve quality��
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GTC Digital is all the great training, research, insights, and direct access to the brilliant minds of the NVIDIA GPU Technology Conference, now online. Join live webinars, training, and Connect with the Experts sessions, or choose from a library of talks, panels, research posters, and demos that you can view on your own schedule, at your own pace. All GTC content >> Explore how the��
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NVIDIA PhysicsNeMo was previously known as NVIDIA SimNet. An article in the latest edition of Science magazine, Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations, describes an emerging branch of fluid dynamics at the intersection of scientific computing and deep learning. It starts with imaging data and discovers the underlying physics and properties of��
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To help manufacturers and developers manage the growing data problem in additive manufacturing, while streamlining production workflows, the team at Dyndrite has developed a new GPU-based platform: Accelerated Computation Engine (ACE), the world��s first GPU-accelerated geometry kernel. Dyndrite is funded by Google��s Gradient Ventures, Carl Bass, ex-CEO of Autodesk, and a number other VC��s.
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Designing a fuel-efficient and stylish vehicle is a massive undertaking that requires teams from many parts of a manufacturer to work in unison. To accelerate the process, Volkswagen, Altair, and Amazon recently showed a proof of concept that can more rapidly test the aerodynamics of vehicle designs in simulation. ��As vehicle manufacturers seek to increase the aerodynamic performance��
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At Supercomputing 2018 in Dallas, Texas, Swiss-based startup Neural Concept displayed their ultra-aerodynamic bike. The bike was developed using the company��s cloud-based machine learning software that leverages the power of NVIDIA GPUs. ��Our program results in designs that are sometimes 5�C20% more aerodynamic than conventional methods. But even more importantly, it can be used in certain��
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Coronary artery disease affects more than two million people annually in the United States, and is the single largest health problem in the world. The condition is normally caused by plaque buildup, which leads the coronary arteries to narrow. To help doctors improve the efficiency of diagnosis, researchers from IBM are using high-performance computing, mathematics, and data to explore a new way��
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PGI Compilers & Tools are used by scientists and engineers developing applications for high-performance computing (HPC). PGI products deliver world-class multicore CPU performance, an easy on-ramp to GPU computing with OpenACC directives, and performance portability across all major HPC platforms. Version 17.10 is available now for users with current PGI Professional support.
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Researchers from University of California, Berkeley developed a deep learning-based method that creates a 3D reconstruction from a single 2d color image. ��Humans have the ability to effortlessly reason about the shapes of objects and scenes even if we only see a single image,�� mentioned Christian H?ne of the Berkeley Artificial Intelligence Research lab. ��The question which immediately arises is��
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Todd Raeker, Research Technology Consultant at the University of Michigan shares how a group of 50 researchers at University of Michigan are using GPUs and OpenACC to accelerate the codes for their data-driven physics simulations. The current versions of the codes use MPI and depend on finer and finer meshes for higher accuracy which are computationally demanding. To overcome the demands��
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Modern computer architectures have a hierarchy of memories of varying size and performance. GPU architectures are approaching a terabyte per second memory bandwidth that, coupled with high-throughput computational cores, creates an ideal device for data-intensive tasks. However, everybody knows that fast memory is expensive. Modern applications striving to solve larger and larger problems can be��
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Cyril Chamenois, co-founder and COO of Elixir Aircraft, shares how they are using NVIDIA technologies to develop the industry��s first aircraft designed using cloud-based applications. Chameonis mentions Elixir is a small start-up with limited resources, and they are taking advantage of using the best-in-class tools on the cloud instead of investing on a large IT infrastructure. The Elixir team is��
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Anne Severt, PhD student at Forschungszentrum J��lich in Germany shares how she is using NVIDIA Tesla K80s and OpenACC with complex geometries to create real-time simulations of smoke propagation to better prepare firefighters for real-life situations �C such as where smoke will be propagating from underground metro stations over time. To learn more, view Anne��s poster from this year��s��
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Oak Ridge National Lab, NVIDIA and PGI launched the OpenACC Hackathon initiative last year to help scientists accelerate applications on GPUs. OpenACC was selected as a primary tool since it offers acceleration without significant programming effort and works great with existing application codes. University of Delaware (UDEL) hosted a five-day Hackathon last week. Selected teams of scientific��
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At the 2016 GPU Technology Conference (GTC), Hyundai presented how they used GPU-accelerated design software to create a full set of virtual materials for the exterior and interior of the Hyundai Genesis G380. David Nikel, Digital Model Manager at Hyundai California Design Studio, explained the challenges involved in creating and refining new realistic materials, and how they share material��
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Zhili Chen, a 3D Graphics Researcher from Adobe Research, shares how they are using TITAN X GPUs and CUDA to create a real-time painting system that simulates the interactions between brush, paint, and canvas at the bristle level. He describes how artists can use the system to draw realistic and vivid digital paintings, by applying the painting techniques they are familiar with��
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Peter Vincent of the Department of Aeronautics at Imperial College London shares how they are using NVIDIA Tesla GPUs to accelerate Computational Fluid Dynamics simulations that will improve the design processes used by companies that design aircrafts or Formula One race cars. Learn more about PyFR, the open-source CFD package developed by Vincent��s lab, that employs new super��
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Flood risk assessment is important in minimizing damages and economic losses caused by flood events. A team of researchers from Vienna University of Technology and visual computing firm VRVis, are using GPUs to run fast simulations of large-scale scenarios, including river flooding, storm-water events and underground flows. The researcher��s primary interest is in decision-making systems��
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James McClure, a Computational Scientist with Advanced Research Computing at Virginia Tech describes how they are using the NVIDIA Tesla GPU-accelerated Titan Supercomputer at Oak Ridge National Laboratory. Their project involves mathematical models combined with 3D visualization to provide insight on how fluids move below the surface of the earth. This can ultimately be used to extract oil or to��
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James McClure, a Computational Scientist with Advanced Research Computing at Virginia Tech shares how his group uses the NVIDIA Tesla GPU-accelerated Titan Supercomputer at Oak Ridge National Laboratory to combine mathematical models with 3D visualization to provide insight on how fluids move below the surface of the earth. McClure spoke with us about his research at the 2015 Supercomputing��
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Taking advantage of the 27 petaflop Titan Supercomputer at Oak Ridge National Laboratory, researchers from Imperial College London��s Department of Aeronautics are attempting to reduce aircraft noise by visualizing how air is forced through engines when planes are in flight. Noise pollution from aircraft is a global policy and health issue. In fact, the scientists from Imperial have previously��
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A team from the Advanced Intelligent Robot Laboratory at the National Taiwan University of Science and Technology used CUDA, GeForce and Kinect Fusion to train a multi-axis robot to autonomously spray paint bicycle frames. Nowadays, autonomous spraying painting is an important process in industries for several reasons. However, programming an industrial robot by the ordinary teaching method��
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Computational Fluid Dynamics (CFD) is a valuable tool to study the behavior of fluids. Today, many areas of engineering use CFD. For example, the automotive industry uses CFD to study airflow around cars, and to optimize the car body shapes to reduce drag and improve fuel efficiency. To get accurate results in fluid simulation it is necessary to capture complex phenomena such as turbulence��
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