Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields

2026-04-23 · Source: cs.LG updates on arXiv.org · Field: Science & Research — Mathematics & Computational Sciences, Engineering & Applied Sciences, Space Science & Astronomy · Depth: Expert, medium

Summary

A new study introduces transferable neural fields, an extension of implicit neural representations (INRs), designed to efficiently model high-dimensional scientific signals across time and simulation ensembles. This approach addresses the limitations of traditional INRs, which suffer from slow convergence and scaling issues in complex scientific settings. By transferring INR features, the method significantly improves signal fidelity and the accuracy of derived physical quantities like density gradients and vorticity. Experiments across controlled transformations and high-fidelity scientific domains, including turbulent flows, fluid-material impact dynamics, and astrophysical systems, demonstrate that transferable features reduce iterations to reach target reconstruction quality by up to an order of magnitude and increase early-stage reconstruction quality by over 10 dB in some cases. This framework supports both continuous spatiotemporal representations and signals indexed by different timesteps or simulations.

Key takeaway

For AI Scientists and Research Scientists working with high-dimensional scientific data, adopting transferable neural fields can drastically reduce training time and improve the fidelity of derived physical quantities. You should consider implementing this encoder-decoder decomposition strategy to amortize fitting costs across related simulations or time-series data, leading to faster convergence and more accurate physical insights compared to training INRs from scratch.

Key insights

Transferable neural fields enable efficient, scalable representation of high-dimensional scientific data by reusing learned features.

Principles

• Shared underlying dynamics motivate feature reuse.
• Encoder-decoder decomposition captures common and specific structures.
• Pretrained encoders accelerate fitting and improve reconstruction.

Method

Decompose an INR into a shared encoder and signal-specific decoders. Train jointly on related signals, then use the pretrained encoder with a new, randomly initialized decoder for unseen signals.

In practice

• Apply to spatiotemporal scientific data.
• Use for turbulent flows, astrophysical systems.
• Improve gradient-based physical accuracy.

Topics

• Neural Fields
• Implicit Neural Representations
• Transferable Features
• Amortized Fitting
• Scientific Signal Modeling

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.