Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

• URL: http://arxiv.org/abs/2609.17440v1
• Date: Tue, 15 Sep 2026 16:46:51 GMT
• Title: Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
• Abstract summary: We present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions.<n>Results demonstrate that the proposed method substantially reduces the number of high-fidelity simulator evaluations.<n>These results highlight RS-MFBO as a scalable, simulator-agnostic approach for cost-constrained black-box optimization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions. The approach integrates Global Sensitivity Analysis (GSA) for dimensionality reduction with a fidelity-augmented Gaussian process that captures correlations between low-cost approximations and expensive high-fidelity evaluations. A cost-aware acquisition strategy, augmented with cooldown and promotion mechanisms, adaptively guides the allocation of samples across fidelities. The framework is validated on two distinct industrial process simulators: a plasmid DNA bioprocess in SuperPro Designer and a green fuel synthesis plant in Aspen HYSYS. Results across diverse economic and physical objectives demonstrate that the proposed method substantially reduces the number of high-fidelity simulator evaluations while maintaining competitive optimization performance compared to single-fidelity baselines. These results highlight RS-MFBO as a scalable, simulator-agnostic approach for cost-constrained black-box optimization.

Related papers

• Convex Optimization for Alignment and Preference Learning on a Single GPU [52.997197698288936]
  Fine-tuning large language models to align with human preferences has driven the success of systems such as Gemini and ChatGPT.<n>Direct Preference Optimization (DPO) offers a simpler alternative but has limitations such as inconsistent ranking accuracy and high dependence on GPU resources.<n>We propose the Convex Optimization for Alignment and Preference Learning Algorithm (COALA), a novel lightweight strategy with strong theoretical guarantees.
  arXiv  Detail & Related papers  (2026-05-22T05:25:00Z)
• Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design [0.34998703934432673]
  We show how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution.<n>We show that employing our proposed multi-fidelity approach leads to $86%$ to $200%$ more constraint compliant solutions given a limited budget.
  arXiv  Detail & Related papers  (2026-03-30T20:36:13Z)
• Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
  LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
  arXiv  Detail & Related papers  (2026-01-10T09:49:45Z)
• Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization [0.0]
  Cut order planning (COP) is a critical challenge in the textile industry, directly impacting fabric utilization and production costs.<n>We propose a novel Quantum-Inspired Deep Reinforcement Learning framework that integrates Long Short-Term Memory networks with Ornstein-Uhlenbeck noise.<n>A comparative analysis reveals that the proposed approach achieves fabric cost savings of up to 13% compared to conventional methods.
  arXiv  Detail & Related papers  (2025-08-13T05:00:50Z)
• HiLAB: A Hybrid Inverse-Design Framework [0.0]
  HiLAB is a new paradigm for inverse design of nanophotonic structures.<n>It addresses multi-functional device design by generating diverse freeform configurations at reduced simulation costs.
  arXiv  Detail & Related papers  (2025-05-23T05:34:56Z)
• Multifidelity Simulation-based Inference for Computationally Expensive Simulators [7.065679767112407]
  We introduce MF-(TS)NPE, a multifidelity approach to neural posterior estimation that uses transfer learning to leverage inexpensive low-fidelity simulations.<n>We further improve simulation efficiency by introducing A-MF-TSNPE, a sequential variant that uses an acquisition function targeting the predictive uncertainty of the density estimator.
  arXiv  Detail & Related papers  (2025-02-12T13:59:22Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Self-focusing virtual screening with active design space pruning [2.853824009425466]
  We propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP) We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization.
  arXiv  Detail & Related papers  (2022-05-03T19:47:30Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)
• Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach [44.25245545568633]
  We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms.
  arXiv  Detail & Related papers  (2020-11-02T06:59:04Z)

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.