New functionality to curate and train DoMINO at scale and validate against a physics-based benchmark suite.
]]>The new release includes new network architectures for external aerodynamics application as well as for climate and weather prediction.
]]>NVIDIA PhysicsNeMo v24.09 delivers utilities to physics-inform training and validation of any model, plus other enhancements.
]]>NVIDIA PhysicsNeMo 24.07 brings new GNN enhancements and application samples for training with large meshes.
]]>NVIDIA PhysicsNeMo 24.01 updates distributed utilities and samples for physics informing DeepONet and GNNs.
]]>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…
]]>This version 23.05 update to the NVIDIA PhysicsNeMo platform expands support for physics-ML and provides minor updates.
]]>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…
]]>NVIDIA Base Command Platform provides the capabilities to confidently develop complex software that meets the performance standards required by scientific computing workflows. The platform enables both cloud-hosted and on-premises solutions for AI development by providing developers with the tools needed to efficiently configure and manage AI workflows. Integrated data and user management simplify…
]]>NVIDIA PhysicsNeMo is now available on NVIDIA LaunchPad. Sign-up for a free, hands-on lab that will teach you how to develop physics-informed machine-learning solutions.
]]>A variety of techniques are currently being developed that capitalize on the efficiency of AI to fight against climate change and achieve net-zero carbon emissions. For power plants, developing techniques to reduce carbon emissions, carbon capture, and storage processes requires a detailed understanding of the associated fluid mechanics and chemical processes throughout the facility.
]]>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…
]]>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.
]]>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…
]]>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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