Inertial Confinement Fusion Forecasting via Large Language Models

Authors: Mingkai Chen, Taowen Wang, shihui cao, James Chenhao Liang, Chuan Liu, Chunshu Wu, Qifan Wang, Ying Nian Wu, Michael Huang, Chuang Ren, Ang Li, Tong Geng, Dongfang Liu
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for Science, Inertial Confinement Fusion

Abstract:
Controlled fusion energy is deemed pivotal for the advancement of human civilization. In this study, we introduce $\textbf{LPI-LLM}$, a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms tailored to address a critical challenge, Laser-Plasma Instabilities ($\texttt{LPI}$), in Inertial Confinement Fusion ($\texttt{ICF}$). Our approach offers several key contributions: Firstly, we propose the $\textit{LLM-anchored Reservoir}$, augmented with a $\textit{Fusion-specific Prompt}$, enabling accurate forecasting of $\texttt{LPI}$-generated-hot electron dynamics during implosion. Secondly, we develop $\textit{Signal-Digesting Channels}$ to temporally and spatially describe the driver laser intensity across time, capturing the unique characteristics of $\texttt{ICF}$ inputs. Lastly, we design the $\textit{Confidence Scanner}$ to quantify the confidence level in forecasting, providing valuable insights for domain experts to design the $\texttt{ICF}$ process. Extensive experiments demonstrate the superior performance of our method, achieving 1.90 CAE, 0.14 $\texttt{top-1}$ MAE, and 0.11 $\texttt{top-5}$ MAE in predicting Hard X-ray ($\texttt{HXR}$) energies emitted by the hot electrons in $\texttt{ICF}$ implosions, which presents state-of-the-art comparisons against concurrent best systems.  Additionally, we present $\textbf{LPI4AI}$, the first $\texttt{LPI}$ benchmark based on physical experiments, aimed at fostering novel ideas in $\texttt{LPI}$ research and enhancing the utility of LLMs in scientific exploration. Overall, our work strives to forge an innovative synergy between AI and $\texttt{ICF}$ for advancing fusion energy.

Decision: reject
Reviewer scores: 5, 3, 8

Reviews and discussion:
<thread>
creation date:
1730676932243

thread_type:
official_review

<thread_object>
creator: reviewer_y3vx

<content>
summary:
In this paper, the authors introduce LPI-LLM, a novel strategy for utilizing large language models (LLMs) in inertial confinement fusion (ICF) experiments. The paper begins with a comprehensive introduction to ICF, providing essential context for understanding the motivation behind this research. The authors leverage the LLM as a repository for general knowledge, employing a combination of scientific (fusion-specific) prompts along with temporal and spatial signals from a signal digestion block as inputs to the LLM. They train a prediction head on top of the LLM to derive the hot-electron energy profile. Additionally, a confidence estimator for the energy profile is developed, which considers both the last layer token embeddings and saliency to enhance trustworthiness. Notably, the authors present a benchmark dataset, LPI4AI, which could be instrumental for the next generation of AI-based ICF research. Extensive validation and ablation studies are conducted across different models to demonstrate their efficacy.

strengths:
The paper is well-written and easy to follow, making it accessible to researchers working with scientific data. The authors have mentioned that the code and LPI4AI benchmark will be released in the future. I believe that this will support reproducibility and further research in this area.

weaknesses:
Refer to Questions

questions:
* Can the authors elucidate more on the spatial encoder block? Are the projections for the prompts “peak,” “trailing,” etc., derived from the LLM itself, followed by a cross-attention mechanism with the temporal encoder’s features?

* How does the confidence assessment engine (CAE) change when the fusion-specific prompt is constructed using only (i) context, (ii) task, or (iii) input descriptors? If only two out of these three descriptors are used, will there be a significant drop in performance?

* For Figure 6 and its counterpart in the supplementary materials, are the authors normalizing the confidence score (C = -H x S) for the plots? If yes, that needs to be clarified in the paper. If not, can the authors explain why the maximum value of that product is close to 1 while the minimum is around 0.90 in certain cases?

* For the LLM baselines (Time-LLM and GPT4TS), can the confidence scanner be implemented? It would be beneficial to find out how confident or under-confident they are on the predictions?

* Can the authors elaborate on the data curation process for LPI4AI?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730700605172

thread_type:
official_review

<thread_object>
creator: reviewer_gyqk

<content>
summary:
This paper presents LPI-LLM, an innovative approach that combines Large Language Models (LLMs) with reservoir computing to predict hot electron dynamics in Inertial Confinement Fusion (ICF). The method extends traditional reservoir computing by integrating time-series LLMs, utilizing domain-specific prompts and summary statistics to enhance predictive performance. A pre-trained LLM is used for feature extraction based on fusion-specific prompts, followed by training a prediction head to estimate hot electron energy with confidence scores. The model also incorporates components designed to capture both temporal and spatial information. The approach is evaluated using a newly developed dataset of laser-driven ICF shots.

strengths:
The authors introduce a method for predicting electron energy in inertial confinement fusion using a Large Language Model (LLM) approach, supported by data collected from experiments on the OMEGA to validate its effectiveness. The use of LLMs for time-series forecasting in this impactful domain is noted as particularly promising. The newly developed LPI4AI dataset is highlighted for its potential to drive innovative research in machine learning. The paper features a comprehensive evaluation, including ablation studies and comparative analyses, to showcase the model's performance.

weaknesses:
The paper fails to establish in depth how the usage of LLMs can create enough richness of methodology to solve a complex problem to its finer details. Based on my experience of LLM usage for problems of comparable complexity, the limited disclosure of prompts is far from convincing. For an LLM to function effectively as an agent for solving complex problems that involve quantitative analysis, it needs an agentic design that integrates sophisticated prompt engineering and structured reasoning methodologies, such as chain-of-thought prompting. Chain-of-thought (CoT) prompting allows an LLM to break down intricate problems into a series of intermediate reasoning steps, mimicking how a human might approach and solve a multi-layered analytical task. This stepwise decomposition helps the model to systematically tackle each part of the problem, enabling clearer and more accurate reasoning. Prompts could be engineered to guide the LLM in sequentially explaining its calculations, assumptions, and decision-making processes. Without implementing such structured approaches, the application of an LLM to complex, analysis-driven problems remains superficial, as the model may produce outputs that hallucinate and lack precision or logical coherence.

This paper has limited meaningful algorithmic contributions and suffers from a method insufficiency or its articulation to address the core problem. This reduces the impact of this paper. This can be better presented by evaluating the system against a wide range of time-series problems with the required details. 

One major concern with Figure 5 is the surprisingly poor performance of baseline models like the vanilla LSTM and Autoformer, which raises questions about whether these models were adequately optimized. Given the proven effectiveness of transformer-based models and LSTMs in similar predictive tasks, their lackluster results suggest potential shortcomings in hyperparameter tuning and model refinement. Without clear details on the training procedures or any effort to optimize these baselines, the comparison appears unconvincing, undermining the reliability of the reported findings.

Additionally, the paper lacks a critical discussion contrasting these models' inherent strengths and weaknesses relative to the proposed method. Providing such insights is essential for readers to assess whether the reported performance differences are due to fundamental advantages of the proposed approach or simply insufficient effort in tuning the baseline models. This omission suggests that the experimental setup may have been incomplete or lacked rigor. To address this, the authors should ensure proper hyperparameter optimization of baseline models, clearly document the tuning process, and include a robust analysis explaining the performance differences. Such enhancements would lend greater credibility to the results and offer readers a more comprehensive understanding of the proposed method's advantages.

It will also be good to explain how the fixed reservoir dynamics in reservoir computing, along with its sensitivity to hyperparameters and limited scalability, impact the model’s adaptability and performance compared to more flexible and fully trainable architectures like transformers and LSTMs, particularly in complex or evolving data environments. The author can then reflect on how reservoir computing could be more impactful for this specific problem despite these shortcomings. Given its lack of adoption, unlike other well-established methods, it is crucial to expand on the topic in detail and establish how this can solve the problem better.

questions:
Are the authors authorized to release the LPI4AI dataset? Will there be any violation of US DOE guidelines in releasing such data arising out of the research conducted with OMEGA?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730758892509

thread_type:
official_review

<thread_object>
creator: reviewer_ydn5

<content>
summary:
This paper presents a method to predict hard X-ray (HXR) energies emitted by the hot electrons in ICF implosions in nuclear physics. The method relies on engineering fusion-specific prompts for LLMs, along with "signal-digesting channels", to predict these time series data. The predictions themselves are then calibrated using a "confidence scanner" to try to characterize the confidence in predictions across the dataset.

strengths:
- The integration of LLMs with reservoir computing for scientific applications appears novel both in the nuclear physics domain and across scientific computing more generally. Additionally, the design of the signal-digesting channels is a novel addition.
- Experimental results show that LPI-LLM achieves superior forecasting accuracy relative to the other data-driven approaches presented.
- The introduction of the LPI4AI benchmark dataset provides a valuable resource for the scientific machine learning community.
- The ablation studies provided in table 2C are useful.

weaknesses:
- The introduction leans heavily on specialized scientific language, potentially making it less accessible for the broader NeurIPS community unfamiliar with ICF or plasma physics.
- The paper does not incorporate or mention any non-ML models that might be commonly used for these prediction tasks in plasma physics. This would provide a meaningful performance benchmark from within the field.
- The confidence scanner’s accuracy is only demonstrated through select examples. However, there is no quantifiable metric provided. Could the authors perform any expected calibration test on these results? This is particularly valuable in light of the fact that the confidence scanner is not shown to produce a meaningful likelihood analytically.

questions:
See weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732329090646

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Responses

comment:
To all reviewers:

Thank you for your detailed and insightful feedback. We have individually responded to your comments and revised our papers based on your comments and suggestions. Our responses and revision of the paper are summarized as follows:

* As suggested by Reviewer yDn5, we have enhanced the accessibility of our introduction by providing a new glossary of specialized terms from both physics and AI domains in Appendix S1 to ensure that broader audiences can follow the core concepts of the paper. In addition, we clarified our comparison with traditional non-ML methods like Particle-In-Cell (PIC) simulations, highlighting both performance and computational efficiency improvements in Section 3 and Appendix S2. Finally, we performed additional experiments to analyze the accuracy of the Confidence Scanner, adding detailed discussion and visualizations in Appendix S7.  
* In response to Reviewer GyQK’s questions, we discuss the major contributions of our algorithm, emphasizing its integration of LLM-based reservoir computing with domain-specific adaptations like Signal-Digesting Channels and the Confidence Scanner. In response to the concern about the ability of LLMs to solve complex physical problems, we clarified in Appendix S2 and Table S1 that our approach leverages inherent capabilities of LLMs, achieving strong performance even without complex reasoning chains such as chain-of-thought prompting. Additional results in Table 2 highlight our method's scalability and efficiency compared to fully trainable architectures like transformers and LSTMs. Lastly, we confirmed that the LPI4AI dataset adheres to all US DOE guidelines, with processed data prepared for release to the research community.  
* Based on suggestions by Reviewer Y3Vx, we have refined the description of the projection block from the LLM in Section 2.2.2. We also conducted new ablation studies in Appendix S5 to fully analyze the impact of different prompt descriptors, adding results to Table S2. Furthermore, we clarified the visualization process for confidence scores in Figure 6 and expanded our experiments to analyze the confidence levels of LLM baseline in Appendix S7. These changes aim to provide a more comprehensive understanding of the method's technical underpinnings and comparative performance.


We conducted additional experiments evaluating our method's performance and enhanced sections of the paper for clarity and rigor. All modifications and related contents are highlighted in green (for reviewer yDn5), red (for reviewer GyQK) or blue (for reviewer Y3Vx) in the revision based on the specific reviewer's comments. 

We look forward to further discussions with you.

Sincerely,  
Authors
</content>
</thread_object>

</thread>
<thread>
creation date:
1732549804979

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Looking for Discussion

comment:
Dear Reviewers,

We deeply appreciate the time and effort you have dedicated to reviewing our work, especially given your demanding schedules. Your insightful feedback is invaluable to us, and we are sincerely grateful for your thoughtful contributions. 

Given that the discussion phase is nearing its conclusion, we would be most grateful for the opportunity to engage in further dialogue with you before it ends. Our goal is to ensure that our responses address your concerns effectively and to explore any additional questions or comments you may have. 

Thank you once again for your time and consideration. We look forward to the possibility of continuing this constructive dialogue.

Warm regards,  
The Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
Thank you for your submission to ICLR. This paper presents LPI-LLM, a method integrating large language models with reservoir computing, for the task of forecasting of laser plasma instabilities for inertial confinement fusion.  

This is a borderline submission. Reviewers agreed that the method seems to perform well empirically, and the LPI4AI dataset is a valuable contribution for the community. However, reviewers also had a number of concerns about this paper. From discussion, reviewers felt that this paper had limited algorithmic contributions, and mostly relied on heuristics without clear underlying principles motivating the method. They were also skeptical about the quality of baseline models. Further, the paper contains a large amount of domain-specific jargon which would be difficult for the broader machine learning audience and hurts the broader accessibility (though I do appreciate the addition of the glossary in Appendix S1). By the end of the rebuttal and discussion, a majority of the authors were unconvinced and had reasonable remaining concerns. Therefore, I recommend this paper for rejection.