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Description

Google Meridian is an open source framework for Marketing Mix Modeling (MMM) designed by Google, aimed at assisting advertisers and analysts in effectively assessing the influence of their marketing initiatives across both online and offline platforms without dependence on cookies or individual user tracking. Central to Meridian is a Bayesian causal-inference model that can process aggregated data—including spend, sales, key performance indicators, reach and frequency, geographical data, seasonality, and external controls—to determine the incremental impact of each marketing channel, such as search, social media, video, and offline media, on overall results, as well as to calculate return on ad spend (ROAS), response curves, and optimal budget distribution. As an open-source tool, it affords users complete visibility into the methodologies and code, empowering them to customize model settings, data inputs, and the underlying assumptions. This level of transparency not only enhances trust but also encourages collaboration among users to refine the model further. Additionally, the open-source nature allows for community contributions, which can lead to continuous improvements and innovations in the framework.

Description

NVIDIA PhysicsNeMo is a publicly available Python-based deep-learning framework designed for the creation, training, fine-tuning, and inference of physics-AI models that integrate physical principles with data, thereby enhancing simulations, developing accurate surrogate models, and facilitating near-real-time predictions in various fields such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate studies, and digital twin technologies. This framework offers powerful, GPU-accelerated capabilities along with Python APIs that are built on the PyTorch platform and distributed under the Apache 2.0 license, featuring a selection of curated model architectures that include physics-informed neural networks, neural operators, graph neural networks, and generative AI techniques, enabling developers to effectively leverage physics-based causal relationships together with empirical data for high-quality engineering modeling. Additionally, PhysicsNeMo provides comprehensive training pipelines that encompass everything from geometry ingestion to the application of differential equations, along with reference application recipes that help users quickly initiate their development workflows. This combination of features makes PhysicsNeMo an essential tool for engineers and researchers seeking to advance their work in physics-driven AI applications.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Google Ads
Google Display & Video 360
PyTorch
Python

Integrations

Google Ads
Google Display & Video 360
PyTorch
Python

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

developers.google.com/meridian

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/physicsnemo

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Product Features

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