Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo si...Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo simulations like FLUKA are labor-intensive and error-prone,relying on manual input file generation and postprocessing.This limits scalability and efficiency.In this work,we present AutoFLUKA,a novel framework that leverages domain knowledge-embedded large language models(LLMs)and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management,and data analysis.AutoFLUKA also integrates Retrieval-Augmented Generation(RAG)and a web-based user-friendly graphical interface,enabling users to interact with the system in real time.Benchmarking against manual FLUKA simulations,AutoFLUKA demonstrated substantial improvements in resolving FLUKA error-related queries,particularly those arising from input file creation and execution.Traditionally,such issues are addressed through expert support on the FLUKA user forum,often resulting in significant delays.The resolution time for these queries was also reduced from several days to under one minute.Additionally,human-induced simulation errors were mitigated,and a high accuracy in key simulation metrics,such as neutron fluence and microdosimetric quantities,was achieved,with uncertainties below 0.001%for large sample sizes.The flexibility of AutoFLUKA was demonstrated through successful application to both general and specialized nuclear scenarios,and its design allows for straightforward extension to other simulation platforms.These results highlight AutoFLUKA’s potential to transform nuclear engineering analysis by enhancing productivity,reliability,and accessibility through AI-driven automation.展开更多
Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inheren...Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.展开更多
基金supported by the US Department of Energy Office of Nuclear Energy Distinguished Early Career Program under contract number DE-NE0009468support is provided by the Texas A&M Institute of Data Science(TAMIDS)Seed Program for AI,Computing,and Data Science。
文摘Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo simulations like FLUKA are labor-intensive and error-prone,relying on manual input file generation and postprocessing.This limits scalability and efficiency.In this work,we present AutoFLUKA,a novel framework that leverages domain knowledge-embedded large language models(LLMs)and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management,and data analysis.AutoFLUKA also integrates Retrieval-Augmented Generation(RAG)and a web-based user-friendly graphical interface,enabling users to interact with the system in real time.Benchmarking against manual FLUKA simulations,AutoFLUKA demonstrated substantial improvements in resolving FLUKA error-related queries,particularly those arising from input file creation and execution.Traditionally,such issues are addressed through expert support on the FLUKA user forum,often resulting in significant delays.The resolution time for these queries was also reduced from several days to under one minute.Additionally,human-induced simulation errors were mitigated,and a high accuracy in key simulation metrics,such as neutron fluence and microdosimetric quantities,was achieved,with uncertainties below 0.001%for large sample sizes.The flexibility of AutoFLUKA was demonstrated through successful application to both general and specialized nuclear scenarios,and its design allows for straightforward extension to other simulation platforms.These results highlight AutoFLUKA’s potential to transform nuclear engineering analysis by enhancing productivity,reliability,and accessibility through AI-driven automation.
基金supported in part by the National Natural Science Foundation of China(Nos.62301510,62271455,and 72474198)the Public Computing Cloud,CUC.
文摘Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.
