Yartificial intelligence solutions continue to make our lives easier. Today, many jobs can be done through digital assistants and more advanced systems. One of the most active companies in this field NVIDIAoffers both individual and corporate solutions thanks to the technologies it has developed.
Finally, the company, which presented these solutions at its event held today, made a show of strength. Giving an example from a project made by a few companies USAtechnology giant with artificial intelligence omniverseIt showed how important it is to plan the 5G infrastructure of a whole city or in the power generation facilities of Turkey.
ARM investigation from the European Union to NVIDIA!
NVIDIA, which bought the ARM company last year, will be subject to investigation by the European Union. Preliminary review is over.
NVIDIA shapes the future with Omniverse
Entered our lives last year omniverse, the company’s real-time graphics simulation platform. Thanks to the project, simulations similar to real life and physically quite accurate can be created in the digital environment. Request NVIDIA Omniverse studies with.
Scientific studies accelerate with NVIDIA Physic-ML module
Working on solutions to be used in various fields NVIDIAintroduced its new physics-based toolset to assist engineers and scientists. The company, which supports molecular studies to accelerate drug studies, will also eliminate global challenges such as climate change with this project.
The module trains neural networks to use the fundamental laws of physics to model the behavior of complex systems in a wide variety of fields. It then uses it in a variety of digital twin applications, from industrial use cases to climate science.
Like most AI-based approaches, Physic-ML includes a data preparation module that helps manage observed or simulated data. It also describes the geometry of the systems it models and the explicit parameters of the space represented by the input geometry. Possible uses are as follows:
- Sampling planner that allows the user to choose an approach such as semi-random sampling or substantial sampling to improve the convergence and accuracy of the trained model.
- Symbolic management to take partial differential equations and build physics-based neural networks Python based API‘s.
- Curated layers and network architectures that have proven effective for physics-based problems.
- PyTorch and TensorFlow The Physics-ML engine, which takes these inputs to train the model using GPU for acceleration cuDNN and multiple GPU and for multi-node scaling NVIDIA Magnum IO.
The GPU-accelerated toolkit provides faster insights with fast turnaround that complements traditional analysis. The module allows users to explore different configurations and scenarios of a system by evaluating the impact of changing its parameters.
High performance module TensorFlow based application, TensorFlow a domain-specific compiler for linear algebra that speeds up models XLAOptimizes performance by taking advantage of . Multiple GPU for scaling Horovod It uses a distributed deep learning training framework.
The last project to use it was a next-generation energy reactor. The researchers, who digitally modeled the reactor exactly, took advantage of this to identify potential problems. Thus, 1.6 billion dollars were saved from the money spent on annual maintenance.
Ericsson built city using Omniverse
The mobile in question infrastructure everything from the positions of trees to the height and composition of buildings is crucial because they are in networks serving smartphones, tablets and millions of other internet-connected devices. 5G wireless affect the signals.
shiftdelete.netThe Stockholm-based company leverages decades of infrastructure and networking expertise, NVIDIA Omniverse Enterprise combined with. Building a digital scale of a city they have contracted for 5G infrastructure Ericssoncreated a realistic city simulation where everything from cars to trees and even the type of material used in buildings is calculated.
This way, the company won’t have to experiment with anticipating and solving environmental and motion-related problems. While this will increase efficiency, it will cause serious savings.