developing and validating advanced Bayesian optimization algorithms

开发和验证先进的贝叶斯优化算法

• 批准号: 2885563
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885563
• 关键词: developing; validating; advanced; Bayesian; optimization

This PhD project is about developing and validating advanced Bayesian optimization algorithms to drive the search for biopharmaceutical manufacturing processes that are more efficient in terms of costs, environmental impact, floorspace usage, etc. This means that drugs will be more accessible and cheaper for patients, while affecting the environment less.The industry partner of this project, Biopharm Services, has developed a market leading computational tool called BioSolve to support drug manufacturers and suppliers in their quest to design more economical and sustainable production processes. BioSolve comprises a detailed Excel-based process economics-mass balance model that can be configured to create digital twins of different biopharmaceutical production processes. An initial Bayesian Optimizer (BO) has been connected to BioSolve, and the goal of this PhD project is to bring this optimizer to a new level.Biopharmaceuticals are pharmaceutical drug products manufactured in, extracted from, or semisynthesized from biological sources. They include, for example, vaccines, gene therapies, and recombinant therapeutic proteins. Although very effective and accurate, important issues of concern are the cost of production of biopharmaceuticals (e.g. expensive resources, need for high purity), the environmental impact of some of the emerging production technologies (e.g. disposables), and the space needed for the production sites.The focus of the project is to advance the BO's capabilities in terms at least one of the following problem properties:1. Quantifying, simulating and accounting for the inherent noise in a drug production process.2. Tackling problems or larger scale in the decision and/or objective space.3. Allowing users to perform interactive multi-objective optimization.All of these three capabilities will require methodological contributions, which can then be validated on real use cases provided by the industry partner (using also the BioSolve simulator) or on synthetic test problems from the global optimization literature (which may need to be modified to account for the various problem features). The research questions to be tackled in the scope of the project are:1. Can we develop a (heterogeneous) noise-handling method (e.g. based on state-of-the-art uncertainty quantification methods) for BO that is able to deal with different types and degrees of noise across the multiple objectives?2. Can we develop a Bayesian multi-objective optimizer (e.g. based on decomposition-based methods) able to solve problems with many mixed-type decision variables (between 20-40 variables of varying type) and many objectives (more than 2)?3. How do we combine the methods developed in 1) and 2), and use them within an interactive optimization framework to guide the search towards a user-preferred part of the search space only?4. What existing synthetic test problems can be used / how do we combine existing synthetic test problems to test the different capabilities of the developed BO?5. Does the developed BO perform robustly on real biopharmaceutical production case studies provided by the industry partner (with BioSolve being used as the evaluation engine)?These are just some examples - the successful student would be able to develop the research programme within the scope of the broad topic area.

这个博士项目是关于开发和验证先进的贝叶斯优化算法，以推动寻找在成本，环境影响，占地面积使用等方面更有效的生物制药制造工艺。这意味着药物对患者来说将更容易获得和更便宜，同时对环境的影响更小。该项目的行业合作伙伴Biopharm Services，BioSolve开发了一种市场领先的计算工具，称为BioSolve，以支持药物制造商和供应商寻求设计更经济和可持续的生产工艺。BioSolve包括一个详细的基于Excel的过程经济学质量平衡模型，可以配置为创建不同生物制药生产过程的数字孪生模型。一个初始的贝叶斯优化器（BO）已经连接到BioSolve，这个博士项目的目标是把这个优化器带到一个新的水平。生物制药是从生物来源中生产，提取或半合成的药物产品。它们包括例如疫苗、基因疗法和重组治疗性蛋白质。虽然非常有效和准确，但重要的关注问题是生物制药的生产成本（例如昂贵的资源，对高纯度的需求），一些新兴生产技术（例如一次性产品）对环境的影响，以及生产场地所需的空间。对药品生产过程中的固有噪声进行量化、模拟和解释.在决策和/或目标空间中解决问题或更大规模。3.允许用户执行交互式多目标优化所有这三种功能都需要方法学贡献，然后可以在行业合作伙伴提供的真实的用例（也使用BioSolve模拟器）或全局优化文献中的综合测试问题（可能需要修改以考虑各种问题特征）上进行验证。在本项目范围内要解决的研究问题是：1。我们能否为BO开发一种（异构）噪声处理方法（例如，基于最先进的不确定性量化方法），能够处理多个目标中不同类型和程度的噪声？2.我们能否开发一个贝叶斯多目标优化器（例如，基于分解的方法），能够解决许多混合型决策变量（20-40个不同类型的变量）和许多目标（超过2个）的问题？3.我们如何将1）和2）中开发的方法联合收割机，并在交互式优化框架内使用它们来引导搜索仅朝向搜索空间的用户偏好部分？4.可以使用哪些现有的综合测试问题/我们如何联合收割机结合现有的综合测试问题来测试开发的BO的不同功能？5.所开发的BO是否在行业合作伙伴提供的真实的生物制药生产案例研究中表现稳健（BioSolve被用作评估引擎）？这些只是一些例子-成功的学生将能够在广泛的主题领域范围内开发研究计划。