Cover Image

Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Oktavian, M. Rizki, Tunga, Anirudh, Bakshi, Amandeep, Mueterthies, Michael J., Gruenwald, J. Thomas, Nistor, Jonathan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors
by: Tunga, Anirudh, et al.
Published: (2024)

Predicting BWR Criticality with Data-Driven Machine Learning Model
by: Oktavian, Muhammad Rizki, et al.
Published: (2024)

A Methodology for Thermal Limit Bias Predictability Through Artificial Intelligence
by: Tunga, Anirudh, et al.
Published: (2026)

LLMize: A Framework for Large Language Model-Based Numerical Optimization
by: Oktavian, M. Rizki
Published: (2025)

An Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency Structure
by: Erkan, Ali, et al.
Published: (2025)

Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning
by: Ruksana, Shiek, et al.
Published: (2026)

From Collapse to Improvement: Statistical Perspectives on the Evolutionary Dynamics of Iterative Training on Contaminated Sources
by: Bakshi, Soham, et al.
Published: (2026)

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning
by: Kim, Jinsu, et al.
Published: (2024)

Prompt Optimization via Adversarial In-Context Learning
by: Do, Xuan Long, et al.
Published: (2023)

Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers
by: Mittal, Sarthak, et al.
Published: (2025)

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending
by: Satheesh, Anirudh, et al.
Published: (2025)

IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization
by: Chen, Zixuan, et al.
Published: (2026)

Enhancing Quantum Federated Learning with Fisher Information-Based Optimization
by: Bhatia, Amandeep Singh, et al.
Published: (2025)

ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models
by: Yin, Yuxuan, et al.
Published: (2024)

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
by: Zhou, Han, et al.
Published: (2023)

Batch Normalization for Neural Networks on Complex Domains
by: Nguyen, Xuan Son, et al.
Published: (2026)

LLM In-Context Recall is Prompt Dependent
by: Machlab, Daniel, et al.
Published: (2024)

Toward an Artificial General Teacher: Procedural Geometry Data Generation and Visual Grounding with Vision-Language Models
by: Nguyen-Truong, Hai, et al.
Published: (2026)

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)

Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
by: Xie, Yuxi, et al.
Published: (2024)

LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
by: Wu, Yuanchen, et al.
Published: (2025)

Linear Feedback Control Systems for Iterative Prompt Optimization in Large Language Models
by: Karn, Rupesh Raj
Published: (2025)

In-Context Learning with Iterative Demonstration Selection
by: Qin, Chengwei, et al.
Published: (2023)

CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems
by: Khalafaoui, Yasser, et al.
Published: (2024)

AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations
by: Seo, Jamin, et al.
Published: (2025)

Prompt Design and Engineering: Introduction and Advanced Methods
by: Amatriain, Xavier
Published: (2024)

Learning on the Manifold: Unlocking Standard Diffusion Transformers with Representation Encoders
by: Kumar, Amandeep, et al.
Published: (2026)

Siegel Neural Networks
by: Nguyen, Xuan Son, et al.
Published: (2025)

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
by: Wang, Ruhan, et al.
Published: (2025)

Iterative Feature Space Optimization through Incremental Adaptive Evaluation
by: Wu, Yanping, et al.
Published: (2025)

ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
by: Orozco, Rafael, et al.
Published: (2024)

ADO: Automatic Data Optimization for Inputs in LLM Prompts
by: Lin, Sam, et al.
Published: (2025)

GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
by: Lv, Rui, et al.
Published: (2025)

A Survey of Automatic Prompt Engineering: An Optimization Perspective
by: Li, Wenwu, et al.
Published: (2025)

PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space
by: Buchnick, Asaf, et al.
Published: (2026)

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
by: Sun, Yuanfu, et al.
Published: (2025)

Prompt Engineering a Prompt Engineer
by: Ye, Qinyuan, et al.
Published: (2023)

Learning to Extract Context for Context-Aware LLM Inference
by: Kim, Minseon, et al.
Published: (2025)

SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration
by: Robani, Mohammad Daffa, et al.
Published: (2025)

Learning Iterative Reasoning through Energy Diffusion
by: Du, Yilun, et al.
Published: (2024)