arXiv subjects

Emmanuel Lujan

Publications and source records attributed to Emmanuel Lujan.

3 recordsLinked to original sources

arXiv2026

Decision-Support and Modeling with Large Language Models for Geothermal Well Arrays

Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide opportunities to enhance energy production capacity and increase fault tolerance. The development and adoption of these emerging geothermal technologies could be accelerated through the recent advances in large language models (LLMs) and high-level high-performance languages. A challenge in LLM-based applications is the reliability of the generated outputs, as they can be prone to subjective biases and hallucinations. This study assesses the potential of cutting-edge LLMs - such as ChatGPT, Gemini, Claude, Grok, and domain-specific models like AskGDR - as expert assistants that can synthesize insightful interpretations of complex geothermal data, as well as improve feature capabilities of geothermal models and numerical software. We developed a novel approach, leveraging Google's recently introduced AI assistant, NotebookLM, to accelerate the generation of unpublished quantitative geothermal benchmarks. The rapid generation of these evaluation instruments is essential for assessing the swiftly evolving capabilities of emerging language model technologies. In particular, we use these benchmarks and LLM-based interviews to analyze opportunities and limitations of two promising technologies: geothermal well arrays and closed-loop coaxial wells. Furthermore, we present a case study illustrating how LLMs can facilitate auto-parallelization of geothermal numerical models. Our analysis emphasizes their application in digital twins and underscores the importance of high-level, high-performance code generation. This line of research could play a transformative role in the geothermal sector by enabling the next-generation of decision-support applications, integrating data analysis, informed recommendations, and more dynamic numerical modeling workflows.

cs.AI↗

arXiv2026

When Structure is Silent: Opportunities for Algorithmic Dispatch in Linear Algebra

Algorithmic dispatch is essential for performance in linear-algebra-intensive systems. A persistent challenge lies in the treatment of structured matrices. Although such matrices are often described as sparse, the term structured is more precise, as it highlights exploitable properties - such as bandedness or triangularity - whose algorithmic advantages extend beyond sparsity alone. When the dispatch strategy leaves these structures unrecognized, valuable opportunities for optimization are lost. Recent advances in generative AI offer the promise of linking these silent structures to more effective algorithmic and architectural choices, supplying much of the missing connective tissue in computational linear algebra. However, AI-synthesized dispatch strategies also raise important questions about their theoretical soundness. This work introduces analytical criteria - grounded in time-complexity analysis - to determine when structure-aware dispatch delivers tangible gains. We examine the overheads of structure detection and data-format conversion, characterizing their impact on speedup and slowdown. We illustrate these concepts through a case study on LU factorization applied to banded matrices stored in a dense format, demonstrating results that align with theoretical bounds and reveal substantial gains in both performance and memory usage. These analyses underscore the need for more intelligent dispatch strategies to recognize and exploit silent structures - an underused path to high-performance linear algebra.

cs.PF↗

arXiv2026

Data-Driven Dynamic Algorithm Dispatch with Large Language Models

We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By combining prompt engineering with LLaMA 3 and a curated performance database, the model learns to synthesize selection heuristics that exploit structural patterns to identify fast algorithmic choices. A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies. This work, developed as part of the DARPA-MIT SmartSolve project, highlights the promise of LLMs for algorithmic discovery and the development of more adaptive, fast linear algebra software.

cs.AI↗