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jNO library released for unified neural operator and foundation model training

A new JAX-native library called jNO has been released, designed to streamline the training of neural operators and foundation models. It offers unified support for both data-driven and physics-informed training methodologies. The library's key feature is a tracing system that allows users to define domains, model calls, and losses in a single symbolic language, compiling them into a unified optimization pipeline for seamless transitions between different training paradigms. AI

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IMPACT Simplifies the development and training of complex neural operators and foundation models by unifying different training approaches.

RANK_REASON Release of a new open-source library for AI model training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 · Christopher Straub ·

    jNO: A JAX Library for Neural Operator and Foundation Model Training

    jNO (jax Neural Operators) is a JAX-native library for neural operators and foundation models with unified support for both data-driven and physics-informed training. Its core design is a tracing system in which domains, model calls, residuals, supervised losses, and diagnostics …