# Inertial Confinement Fusion Forecasting via Large Language Models

arXiv: https://arxiv.org/abs/2407.11098 · HTML: https://mlsanity.com/paper/2407.11098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 95 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
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- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
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- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
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- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://mlsanity.com/paper/2305.19187/) (2023)
- [Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models](https://mlsanity.com/paper/2307.10236/) (2023)
- [Reservoir Computing meets Recurrent Kernels and Structured Transforms](https://mlsanity.com/paper/2006.07310/) (2020)

## Abstract (arXiv)

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