AI RESEARCH
Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields
arXiv CS.LG
•
ArXi:2604.19979v1 Announce Type: new 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.