Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes NeurOLight, a variant of neural operators that is suitable to emulate optical devices efficiently. While the core behavior of the proposed NeurOLight is similar to the Fourier neural operators (FNOs), the authors introduce several techniques for optical simulations, including the scale-adaptation for merging variant domains, wave priors for Maxwell PDE encoders, masked image modeling for the light source, cross-shaped FNO that separates the horizontal and vertical pattern predictions, and superposition-based data augmentations. These all techniques for NeurOLight can streamline the complexity of the model as well as generalize the predictive performance. The authors show the proposed NeurOLight outperforms other baselines such as U-Net and FNO. They also provide some ablation studies that can support the proposed techniques are indeed helpful.
strengths_and_weaknesses: The paper has many merits: the introduced ad-hoc techniques are convincing for the targeted domain, the writing is clear, and the experimental validation is solid. The paper also demonstrates several interesting results including spectrum analysis and domain transfer learning. Without a doubt, it is a good application paper for optical device simulations.

My main concern is that the target domain seems to be a niche problem for NeurIPS audiences. Because the proposed techniques are generally applicable to such a niche domain, the relevance and significance of this paper are not entirely clear.

Overall, I think this paper is worthy of publication at some venues (maybe a PDE-related workshop or computational physics journals), but the venue does not have to be NeurIPS main conference.

questions: The proposed method seems to be sufficiently solid and complete for its purpose. What prevents me from recommending the clear acceptance of this paper is that, as I mentioned in Strengths and Weaknesses, the main limitation of this paper is that the targeted problem is niche.
limitations: The authors do not explicitly state the limitations of their proposed method.

As I mentioned in Strengths and Weaknesses, I think that the main limitation of this paper is that the targeted problem is less relevant to the NeurIPS audiences.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a neural operator that jointly models multiple parameters of EM simulation. Given input light wavelength, permittivity, and domain/material properties, the goal here is to establish a parametric mapping from the inputs to the output EM field via a neural network. To this end, the input domains are first normalized in terms of spatial scales and resolutions. Then, the normalized inputs are encoded as wave prior similar to the Fourier positional encoding. Following the masked image modeling allows us to cast the challenging optics simulation problem into a synthesis problem for missing regions given the input light. The network architecture for doing that is inspired by the Fourier network with a separable modification applied to that: using two 1D FFT instead of having one 2D FFT. The authors also propose an augmentation method that uses a linear combination of input lights/output fields as additional inputs/outputs. 
strengths_and_weaknesses: Overall I like this paper. It tackles an important, but a challenging problem with novel solutions. Execution is also excellent. I hope below comments could be helpful to refine the paper.
 
Strength
- The paper tackles an emerging problem of learning-based optics simulation. The proposed method achieves SoTA performance on the device level simulation in terms of both accuracy and speed. 
- The paper is very well written with clear descriptions and figures. 
- Execution of the paper is of high quality. 

Weaknesses
- There is no evaluation on the overfitting which often occurs in neural optical operators.
- Evaluation is limited on a device scale as mentioned by the authors.
- The comparison of the proposed method running on a GPU is done against FDFD method running on CPUs as described in Fig1.
- L104: It would be helpful to write detailed forms A,x, and b in the main paper  
questions: - The wave prior method reminds me of the positional encoding which has been extensively used in coordinate-baesd neural implicit functions. I wonder how these two could be related.

- The authors compare the proposed method with FDFD solution that runs on CPUs instead of GPU. Is this because the FDFD on GPUs is challenging to be adopted for this problem?  Discussion on this part would be helpful for general readers to understand the computational challenges in EM simulation.

 
limitations: The authors adequately addressed the limitations.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposed a physics-agnostic light field prediction framework, called NeurOLight, that consists of a joint PDE encoder and an efficient cross-shaped neural operator backbone. A superposition-based mixup technique is developed to dynamically boost the data efficiency and generalization during the training of NeurOLight. Evaluation results show that the proposed method significantly outperforms the existing methods. 
strengths_and_weaknesses: Strengths:
[1] The idea is novel and interesting. It proposed a physics-agnostic light field prediction framework that can improve the efficiency and accuracy of learning parametric Maxwell PDEs. 
[2] It is the first AI-based framework that can learn the terahertz light propagation inside photonic devices that generalizes to different domains.
[3] The proposed framework significantly outperforms the state of the arts with an average of 53.8% lower prediction error.

Weakness:
[1] It is not clear how much prior knowledge we should have for PDE encoder. If we do not have any prior knowledge in some applications, how does the proposed method work?
[2] It is better to report the averaged results with multiple random seeds in Table 1. 
questions: I am wondering if you could introduce the model architecture of the PDE encoder in more detail? Shall we know a PDE formula?
limitations: Yes, they have.
review_title: null

Meta-review: metareview: The authors propose a domain-specific extension of neural operators that is appropriate for photonics applications. This is an interesting application of neural operators which demonstrates the usefulness of building in physical priors. Some reviewers expressed concern about the topic being too far outside the usual focus of NeurIPS, but there is also an upside to introducing novel application areas to the NeurIPS community. All reviewers agreed the work was of high quality and worth accepting, so I recommend acceptance.
confidence: Less certain
Decision: Accept