Hybrid Cold-Start Recommender System for Closure Model Selection in Multiphase Flow Simulations

• URL: http://arxiv.org/abs/2604.09112v1
• Date: Fri, 10 Apr 2026 08:46:30 GMT
• Title: Hybrid Cold-Start Recommender System for Closure Model Selection in Multiphase Flow Simulations
• Abstract summary: This work formulates closure model selection as a cold-start recommender system problem in a high-cost scientific domain.<n>We propose a hybrid recommendation framework that combines (i) metadata-driven case similarity and (ii) collaborative inference via matrix completion.<n>The methodology is evaluated on 13,600 simulations across 136 validation cases and 100 model combinations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Selecting appropriate physical models is a critical yet difficult step in many areas of computational science and engineering. In multiphase Computational Fluid Dynamics (CFD), practitioners must choose among numerous closure model combinations whose performance varies strongly across flow conditions. Sub-optimal choices can lead to inaccurate predictions, simulation failures, and wasted computational resources, making model selection a prime candidate for data-driven decision support. This work formulates closure model selection as a cold-start recommender system problem in a high-cost scientific domain. We propose a hybrid recommendation framework that combines (i) metadata-driven case similarity and (ii) collaborative inference via matrix completion. The approach enables case-specific model recommendations for entirely new CFD cases using their descriptive features, while leveraging historical simulation results from similar cases. The methodology is evaluated on 13,600 simulations across 136 validation cases and 100 model combinations. A nested cross-validation protocol with experiment-level holdout is employed to rigorously assess generalisation to unseen flow scenarios under varying levels of data sparsity. Recommendation quality is measured using ranking-based metrics and a domain-specific regret measure capturing performance loss relative to the per-case optimum. Results show that the proposed hybrid recommender consistently outperforms popularity-based and expert-designed reference models and reduces regret across the investigated sparsities. These findings demonstrate that recommender system methodology can effectively support complex scientific decision-making tasks characterised by expensive evaluations, structured metadata, and limited prior observations.

Related papers

• Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models [5.102380962405881]
  Conditional generative models have emerged as powerful tools for sampling from target conditional distributions.<n>As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the task, data, or input condition.<n>We propose an optimal model averaging framework for conditional generative models, allowing candidate generators to be combined even when they are accessible only through conditional samples.
  arXiv  Detail & Related papers  (2026-07-05T15:38:35Z)
• Adaptive Gaussian Process Search for Simulation-Based Sample Size Estimation in Clinical Prediction Models: Validation of the pmsims R Package [0.45393070061887714]
  We present and validate pmsims, an R package that uses Gaussian process surrogate modelling to provide a flexible simulation-based framework for sample size determination.<n>We benchmarked the best-performing pmsims engine against existing analytical (pmsampsize) and simulation-based (samplesizedev) methods.
  arXiv  Detail & Related papers  (2026-03-24T19:53:37Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation [98.95383921866096]
  We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
  arXiv  Detail & Related papers  (2022-08-22T14:04:56Z)
• fETSmcs: Feature-based ETS model component selection [8.99236558175168]
  We propose an efficient approach for ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We evaluate our approach on the widely used forecasting competition data set M4 in terms of both point forecasts and prediction intervals.
  arXiv  Detail & Related papers  (2022-06-26T13:52:43Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Top-N Recommendation with Counterfactual User Preference Simulation [26.597102553608348]
  Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. In this paper, we propose to reformulate the recommendation task within the causal inference framework to handle the data scarce problem.
  arXiv  Detail & Related papers  (2021-09-02T14:28:46Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications [0.0]
  We describe three state-of-the-art basis-adaptive approaches for sparse chaos expansions. We conduct an extensive benchmark in terms of global approximation accuracy on a large set of computational models. We introduce a novel solver and basis adaptivity selection scheme guided by cross-validation error.
  arXiv  Detail & Related papers  (2020-09-10T12:13:57Z)
• Amortized Bayesian model comparison with evidential deep learning [0.12314765641075436]
  We propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work.
  arXiv  Detail & Related papers  (2020-04-22T15:15:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.