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.

翻译：神经场（即隐式神经表示，INR）为连续几何建模提供了强大框架，但在高维科学场景中其效能受限于收敛缓慢和扩展性挑战。本研究将INR模型扩展至处理时空与多变量信号，并展示了如何跨科学信号传递INR特征，从而以摊销方式实现时间序列与系综运行的高效可扩展表示。在受控变换场景（如合成场的几何变换与局部扰动）及高保真科学领域（包括湍流、流体-材料冲击动力学及天体物理系统）中，我们证明可传递特征不仅能提升信号保真度，还能改善衍生几何与物理量（如密度梯度和涡量）的准确性。具体而言，可传递特征可将达到目标重建质量所需的迭代次数减少一个数量级，将早期重建质量提升数分贝（部分情况下增益超10分贝），并持续改善基于梯度的物理精度。