NVIDIA ModulusNVIDIA
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QMSys GUMQualisyst
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About
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly.
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About
The QMSys GUM Software is suitable for the analysis of the uncertainty of physical measurements, chemical analyses and calibrations. The software uses three different methods to calculate the measurement uncertainty. GUF Method for linear models, this method is applied to linear and quasi-linear models and corresponds to the GUM Uncertainty Framework. The software calculates the partial derivatives (the first term of a Taylor series) to determine the sensitivity coefficients of the equivalent linear model and then calculates the combined standard uncertainty in accordance with the Gaussian error propagation law. GUF Method for nonlinear models, this method is provided for nonlinear models with the symmetric distribution of the result quantities. In this method, a series of numerical methods are used, e.g. nonlinear sensitivity analysis, second and third-order sensitivity indices, quasi-Monte Carlo with Sobol sequences.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Organizations looking for a powerful Physics Machine Learning platform
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Audience
Companies searching for a solution to analyze the uncertainty of physical measurements
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationNVIDIA
Founded: 1993
United States
developer.nvidia.com/modulus
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Company InformationQualisyst
Founded: 1994
Bulgaria
www.qsyst.com/qualisyst_en.htm
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Categories |
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Statistical Analysis Features
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization
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Integrations
No info available.
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Integrations
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