Ceyron/trainax
Training methodologies for autoregressive neural operators/emulators in JAX.
This project helps scientists and engineers working with complex simulations or physical systems create faster, data-driven approximations. It takes in real or simulated time-dependent data (like sensor readings or simulation outputs) and uses it to train a neural network. The output is a highly optimized "neural emulator" that can predict how a system evolves over time much quicker than traditional simulators.
No commits in the last 6 months.
Use this if you need to rapidly predict the behavior of time-dependent physical systems or simulations without running computationally expensive full models.
Not ideal if you are looking for a general-purpose machine learning library or if your problem doesn't involve emulating time-dependent physical processes.
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Language
Python
License
MIT
Category
Last pushed
Nov 05, 2024
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