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Researcher profile

Rong Lu

Rong Lu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
ApplicationsArtificial Intelligenceeess.SYMachine LearningMethodologyphysics.atom-phquant-ph

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Published work

4 published item(s)

preprint2026arXiv

Coherent Two-State Oscillations in False Vacuum Decay Regimes

Coherent two-state oscillations are observed in numerical simulations of the one-dimensional transverse-longitudinal-field Ising model (TLFIM) within false vacuum decay regimes. Starting from the false vacuum (a nearly fully polarized ferromagnetic state), we show that in moderate-sized systems, at resonances $h\approx 2J/n$ (with longitudinal field $h$, transverse field $J$, and an integer $n$), the expected decay can give way to coherent oscillations between the false vacuum and a symmetric resonant state. The oscillation frequency, i.e., the tunneling splitting, is observed notably to exhibit a superradiant-like $\sqrt{L}$ enhancement, as confirmed by a Schrieffer-Wolff analysis. In large chains, coherence remains for $n\gtrsim L/2$ due to bubble-size blockade and is robust against stronger transverse fields; for small $n$, long-range interactions can stabilize the oscillations by lifting multi-bubble degeneracies, establishing a robust many-body coherence mechanism beyond perturbative and finite-size limits.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data

We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Application of machine learning to gas flaring

Currently in the petroleum industry, operators often flare the produced gas instead of commodifying it. The flaring magnitudes are large in some states, which constitute problems with energy waste and CO2 emissions. In North Dakota, operators are required to estimate and report the volume flared. The questions are, how good is the quality of this reporting, and what insights can be drawn from it? Apart from the company-reported statistics, which are available from the North Dakota Industrial Commission (NDIC), flared volumes can be estimated via satellite remote sensing, serving as an unbiased benchmark. Since interpretation of the Landsat 8 imagery is hindered by artifacts due to glow, the estimated volumes based on the Visible Infrared Imaging Radiometer Suite (VIIRS) are used. Reverse geocoding is performed for comparing and contrasting the NDIC and VIIRS data at different levels, such as county and oilfield. With all the data gathered and preprocessed, Bayesian learning implemented by MCMC methods is performed to address three problems: county level model development, flaring time series analytics, and distribution estimation. First, there is heterogeneity among the different counties, in the associations between the NDIC and VIIRS volumes. In light of such, models are developed for each county by exploiting hierarchical models. Second, the flaring time series, albeit noisy, contains information regarding trends and patterns, which provide some insights into operator approaches. Gaussian processes are found to be effective in many different pattern recognition scenarios. Third, distributional insights are obtained through unsupervised learning. The negative binomial and GMMs are found to effectively describe the oilfield flare count and flared volume distributions, respectively. Finally, a nearest-neighbor-based approach for operator level monitoring and analytics is introduced.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Should univariate Cox regression be used for feature selection with respect to time-to-event outcomes?

IMPORTANCE: Time-to-event outcomes are commonly used in clinical trials and biomarker discovery studies and have been primarily analyzed using Cox proportional hazards models. But it's unclear which statistical models should be recommended for feature selection tasks when time-to-event outcomes are of the primary interest. OBJECTIVE: To explore if Gaussian regression of log-transformed survival time could outperform Cox proportional hazards models in feature selection. DESIGN: In this simulation study, the true models are multivariate Cox proportional hazards models with 10 covariates. For all feature selection comparisons, it's assumed that only 5 out the 10 true features are observed/measured for all model fitting, along with 5 random noise features. Each sample size and censoring rate scenario is explored using 10,000 simulation datasets. Different statistical models are applied to the same dataset to estimate feature effects. Model performance is compared using sensitivity, specificity, and accuracy of effect size ranking. RESULTS: When features are independent and the true models are multivariate Cox proportional hazards models, Gaussian regression of log-transformed survival time (response variable) with only two covariates outperformed both the univariate Cox proportional hazards model and logistic regression in feature selection, in terms of not only higher sensitivity, comparable specificity, but also higher accuracy of effect size ranking, regardless of the sample size and censoring rate values. CONCLUSIONS AND RELEVANCE: This study demonstrates the importance of including Gaussian regression of log-transformed survival time in feature selection practice for time-to-event outcomes.

0 citations0 reviewsNo code linkedOpen access