Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework

• URL: http://arxiv.org/abs/2511.07702v1
• Date: Wed, 12 Nov 2025 01:12:13 GMT
• Title: Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
• Authors: Meraj Hassanzadeh, Ehsan Ghaderi, Mohamad Ali Bijarchi, Siamak Kazemzadeh Hannani,
• Abstract summary: This paper introduces a novel framework leveraging cutting-edge Scientific Machine Learning (Sci-ML) methodologies.<n>The proposed method provides instantaneous solutions to a spectrum of complex, multidimensional optimization problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multidimensional optimization has consistently been a critical challenge in engineering. However, traditional simulation-based optimization methods have long been plagued by significant limitations: they are typically capable of optimizing only a single problem at a time and require substantial computational time for meshing and numerical simulation. This paper introduces a novel framework leveraging cutting-edge Scientific Machine Learning (Sci-ML) methodologies to overcome these inherent drawbacks of conventional approaches. The proposed method provides instantaneous solutions to a spectrum of complex, multidimensional optimization problems. A micromixer case study is employed to demonstrate this methodology. An agent, operating on a Deep Reinforcement Learning (DRL) architecture, serves as the optimizer to explore the relationships between key problem parameters. This optimizer interacts with an environment constituted by a parametric Physics-Informed Neural Network (PINN), which responds to the agent's actions at a significantly higher speed than traditional numerical methods. The agent's objective, conditioned on the Schmidt number is to discover the optimal geometric and physical parameters that maximize the micromixer's efficiency. After training the agent across a wide range of Schmidt numbers, we analyzed the resulting optimal designs. Across this entire spectrum, the achieved efficiency was consistently greater than the baseline, normalized value. The maximum efficiency occurred at a Schmidt number of 13.3, demonstrating an improvement of approximately 32%. Finally, a comparative analysis with a Genetic Algorithm was conducted under equivalent conditions to underscore the advantages of the proposed method.

Related papers

• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• 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)
• Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization [0.0]
  This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyper- parameters in a machine learning model. The behavior of the presented model, specifically against the changes in its design parameters, is investigated in both machine learning and global function optimization applications.
  arXiv  Detail & Related papers  (2023-03-03T21:10:17Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• A Globally Convergent Gradient-based Bilevel Hyperparameter Optimization Method [0.0]
  We propose a gradient-based bilevel method for solving the hyperparameter optimization problem. We show that the proposed method converges with lower computation and leads to models that generalize better on the testing set.
  arXiv  Detail & Related papers  (2022-08-25T14:25:16Z)
• Meta-Learning Digitized-Counterdiabatic Quantum Optimization [3.0638256603183054]
  We tackle the problem of finding suitable initial parameters for variational optimization by employing a meta-learning technique using recurrent neural networks. We investigate this technique with the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA) The combination of meta learning and DC-QAOA enables us to find optimal initial parameters for different models, such as MaxCut problem and the Sherrington-Kirkpatrick model.
  arXiv  Detail & Related papers  (2022-06-20T18:57:50Z)
• Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters [0.0]
  Robust design optimization is traditionally considered when uncertainties are mainly affecting the objective function. The resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II) The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles.
  arXiv  Detail & Related papers  (2022-03-03T20:23:18Z)
• Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients [51.42959998304931]
  A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. We propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme.
  arXiv  Detail & Related papers  (2021-04-25T22:43:51Z)
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm [97.66038345864095]
  We propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG) Specifically, we first formulate hyperparameter optimization as an A-based constrained optimization problem. Then, we use the average zeroth-order hyper-gradients to update hyper parameters.
  arXiv  Detail & Related papers  (2021-02-17T21:03:05Z)
• Using models to improve optimizers for variational quantum algorithms [1.7475326826331605]
  Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. We introduce two optimization methods and numerically compare their performance with common methods in use today.
  arXiv  Detail & Related papers  (2020-05-22T05:23:23Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)

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.