A survey of machine learning techniques in structural and multidisciplinary optimization

Black-box optimization methods like Bayesian optimization are often employed in cases where the underlying objective functions and their gradient are complex, expensive to evaluate, or unavailable in closed form, making it difficult or impossible to use traditional optimization techniques. Fixed-wing drone design problems often face this kind of situations. Moreover in the literature multi-fidelity strategies allow to consistently reduce the optimization cost for mono-objective problems. The purpose of this paper is to propose a multi-fidelity Bayesian optimization method that suits to multi-objective problem solving. In this approach, low-fidelity and high-fidelity objective functions are used to build co-Kriging surrogate models which are then optimized using a Bayesian framework. By combining multiple fidelity levels and objectives, this approach efficiently explores the solution space and identifies the set of Pareto-optimal solutions. First, four analytical problems were solved to assess the methodology. The approach was then used to solve a more realistic problem involving the design of a fixed-wing drone for a specific mission. Compared to the mono-fidelity strategy, the multi-fidelity one significantly improved optimization performance. On the drone test case, using a fixed budget, it allows to divide the inverted generational distance metric by 6.87 on average. 

Towards a multi-fidelity & multi-objective Bayesian optimization efficient algorithm

Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study

/pdf/all-at-once-formulation-integrating-pseudo-spectral-optimal-2sf543sk.pdf

All-At-Once formulation integrating pseudo-spectral optimal control for launch vehicle design and uncertainty quantification

A review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages. 

Equation-based and data-driven modeling: Open-source software current state and future directions

/pdf/dymos-a-python-package-for-optimal-control-of-2z50e11hdz.pdf

dymos: A Python package for optimal control of multidisciplinary systems

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dymos: A Python package for optimal control of multidisciplinary systems