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…
]]>Lattice structures are naturally and artificially made designs that are important in many scientific fields, such as materials science, aerospace engineering, and biomedical engineering. They are made by repeating patterns that connect smaller truss structures and yield a high strength-to-weight ratio. The rise of 3D printing within additive manufacturing has highlighted the significance of…
]]>From credit card transactions, social networks, and recommendation systems to transportation networks and protein-protein interactions in biology, graphs are the go-to data structure for modeling and analyzing intricate connections. Graph neural networks (GNNs), with their ability to learn and reason over graph-structured data, have emerged as a game-changer across various domains. However…
]]>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…
]]>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…
]]>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.
]]>NVIDIA PhysicsNeMo was previously known as NVIDIA SimNet. Simulations are pervasive in every domain of science and engineering, but they often have constraints such as large computational times, limited compute resources, tedious manual setup efforts, and the need for technical expertise. Neural networks not only accelerate simulations done by traditional solvers, but also simplify simulation…
]]>