Neural networks in Wolfram Mathematica

In summary, the speaker asks if it is possible to create meaningful neural networks using Wolfram Mathematica for scientific research purposes, such as solving differential equations in physics. They also question if it is just a toy or teaching aid for specific tasks. The other person responds that they are unsure of the efficiency of Mathematica with neural networks, but there is ongoing research on using AI for physics problems.
  • #1
Vrbic
407
18
TL;DR Summary
Is it reasonable to do specific neural networks in WM?
Hello,
I have a question about the use of functions and overall creation of neural networks in the Wolfram Mathematica (WM) program. I wonder if it is realistic to make meaningful neural networks usable at least partially for scientific research in WM? By scientific research, I do not mean the study of neural networks directly, but their application to some specific mathematical or physical problems. Something like using NDSolve to solve the differential equations of e.g. Einstein's relativity, which may in turn lead to a new discovery or in general to the preparation of a scientific paper.

Or is it just a toy or a teaching aid for very specific tasks such as converting handwritten digits to digital digits shown in the WM tutorial?

Thank you for your opinions.
 
Physics news on Phys.org

What is a neural network and how does it work?

A neural network is a type of machine learning algorithm that attempts to mimic the functioning of the human brain. It consists of interconnected nodes, or neurons, that process information and make predictions based on the data they receive. The network learns and improves over time by adjusting the strength of connections between neurons.

How can I create a neural network in Wolfram Mathematica?

Wolfram Mathematica has built-in functions for creating and training neural networks. The most common way is to use the NetChain function, which allows you to specify the structure and parameters of the network. You can also use the NetTrain function to train the network using your data.

What types of neural networks are supported in Wolfram Mathematica?

Wolfram Mathematica supports a variety of neural network architectures, including feedforward networks, recurrent networks, convolutional networks, and generative adversarial networks. It also has specialized functions for working with different types of data, such as images, text, and time series.

Can I visualize and analyze the performance of my neural network in Wolfram Mathematica?

Yes, Wolfram Mathematica has built-in functions for visualizing and analyzing the performance of your neural network. You can use the NetGraph function to visualize the structure of your network, and the NetMeasurements function to evaluate its performance on a given dataset.

Are there any resources available for learning more about neural networks in Wolfram Mathematica?

Yes, Wolfram offers a variety of resources for learning about neural networks in Mathematica, including documentation, tutorials, and online courses. You can also access the Wolfram Neural Network Repository, which contains pre-trained networks and examples of their applications.

Similar threads

Mathematica Can Python and R be used for data science techniques?
    • MATLAB, Maple, Mathematica, LaTeX
Replies
1
Views
1K
Current and Future AI Threats to Humanity and the Human Response
    • Computing and Technology
Replies
10
Views
2K
List of Interactive Programs/Software/Applets
    • Other Physics Topics
Replies
34
Views
52K
Requesting suggestions for languages, libraries, and architectures for parallel (and sometimes non parallel) numerical and scientific computations
    • Programming and Computer Science
Replies
8
Views
2K
Meet jtbell: Meet a Mentor Series Interview
    • General Discussion
Replies
12
Views
5K

Hot Threads

Recent Insights

Back
Top