FieldSeer I: Physics-Guided World Models for Long-Horizon Electromagnetic Dynamics under Partial Observability
On the map: World models → Latent predictive world models
Abstract (arXiv)
We introduce FieldSeer I, a geometry-aware world model that forecasts electromagnetic field dynamics from partial observations in 2-D TE waveguides. The model assimilates a short prefix of observed fields, conditions on a scalar source action and structure/material map, and generates closed-loop rollouts in the physical domain. Training in a symmetric-log domain ensures numerical stability. Evaluated on a reproducible FDTD benchmark (200 unique simulations, structure-wise split), FieldSeer I achieves higher suffix fidelity than GRU and deterministic baselines across three practical settings: (i) software-in-the-loop filtering (64x64, P=80->Q=80), (ii) offline single-file rollouts (80x140, P=240->Q=40), and (iii) offline multi-structure rollouts (80x140, P=180->Q=100). Crucially, it enables edit-after-prefix geometry modifications without re-assimilation. Results demonstrate that geometry-conditioned world models provide a practical path toward interactive digital twins for photonic design.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 4 of its 8 references that are in the map. First posted on arXiv 5 Dec 2025.
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4 of 8 references.
References · 4
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
- 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
- 2019Global optimization of dielectric metasurfaces using a physics-driven neural networkcited by 30
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