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

2026-04-21

AI Summary

Traditional neural fields exhibit slow convergence and limited scalability when modeling high-dimensional scientific signals, particularly in efficiently handling spatiotemporal and multivariate data. This work proposes a transferable neural field feature mechanism that integrates implicit neural representations with amortized optimization strategies to enable rapid fitting across time steps and ensemble simulations. The approach dramatically accelerates the reconstruction process, reducing the required number of iterations by an order of magnitude while improving early-stage reconstruction quality by over 10 dB. Furthermore, it consistently enhances the accuracy of key physical quantities—such as density gradients and vorticity—across diverse scientific scenarios including turbulence, fluid–material interactions, and astrophysical simulations.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Neural fields, also known as implicit neural representations (INRs), offer a powerful framework for modeling continuous geometry, but their effectiveness in high-dimensional scientific settings is limited by slow convergence and scaling challenges. In this study, we extend INR models to handle spatiotemporal and multivariate signals and show how INR features can be transferred across scientific signals to enable efficient and scalable representation across time and ensemble runs in an amortized fashion. Across controlled transformation regimes (e.g., geometric transformations and localized perturbations of synthetic fields) and high-fidelity scientific domains-including turbulent flows, fluid-material impact dynamics, and astrophysical systems-we show that transferable features improve not only signal fidelity but also the accuracy of derived geometric and physical quantities, including density gradients and vorticity. In particular, transferable features reduce iterations to reach target reconstruction quality by up to an order of magnitude, increase early-stage reconstruction quality by multiple dB (with gains exceeding 10 dB in some cases), and consistently improve gradient-based physical accuracy.

Problem

• neural fields
• implicit neural representations
• scientific signals
• spatiotemporal modeling
• scalability

Innovation

• Transferable Neural Fields
• Implicit Neural Representations
• Amortized Fitting
• Spatiotemporal Modeling
• Scientific Signal Reconstruction