Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing

We formulate offline tuning of Model Predictive Path Integral (MPPI) controllers as a chance-constrained black-box optimization problem and develop an update-aware framework based on Matrix Adaptation Evolution Strategy (MA-ES). We use statistical information from noisy controller evaluations to determine when the evolutionary update is sufficiently reliable. When the recombination direction is certified, a derivative-free nonmonotone … Read more

Twist Without Tangle: Flutter Suppression of Thin-Walled Wing-Engine Systems via Curvilinear Fiber Path Tailoring and Cross-Section Optimization

Flutter is traditionally delayed by modifying either a structure’s geometry or its stiffness distribution. Here, we show that allowing both to evolve simultaneously can unlock a fundamentally different route to aeroelastic stability. We concurrently optimize the cross-sectional geometry and fiber paths of a composite thin-walled wing–engine system to maximize flutter onset. The wing structure is … Read more

The Exclusion-Inclusion Gap for Evaluating Finite Pareto Approximations in Multiobjective Stochastic Optimization

We introduce the exclusion–inclusion gap for evaluating finite Pareto approximations constructed from stochastic objective estimates. The gap measures the maximum of two objective-space errors associated with excluding Pareto points from the returned set’s true upper image and including dominated points in the returned set’s true lower image. The terms in its approximation–estimation error decomposition admit … Read more

Assessing Green Hydrogen Production via Offshore Wind in the Dutch North Sea: Complementing Techno-Economic Simulation With Machine Learning and Optimization

As countries seek to decarbonize their energy systems, green hydrogen has emerged as a promising energy carrier. This paper studies the production of green hydrogen from offshore wind in the Dutch North Sea, with particular emphasis on understanding how system design decisions and uncertain parameters affect key performance indicators. The analysis builds on a detailed … Read more

A Framework for Explainable Knowledge Generation with Expensive Sample Evaluations

Real world problems often require complex modeling and computation efforts to be effectively addressed. Relying solely on data-driven approaches without integrating physics-based models can result in limited predictive capabilities. Even advanced techniques such as deep learning may be impractical for decision-makers due to the lack of data and challenges in justifying and explaining results. In … Read more

An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluations

An algorithm based on the interior-point methodology for solving continuous nonlinearly constrained optimization problems is proposed, analyzed, and tested. The distinguishing feature of the algorithm is that it presumes that only noisy values of the objective and constraint functions and their first-order derivatives are available. The algorithm is based on a combination of a previously … Read more

Black-box optimization for the design of a jet plate for impingement cooling

In this work, we propose a novel black-box formulation of the impingement cooling system for a nozzle in a gas turbine. Leveraging on a well-known model that correlates the design features of the cooling system with the efficiency parameters, we develop NOZZLE, a new constrained black-box optimization formulation for the jet impingement cooling design. Then … Read more

Inter-DS: A cost saving algorithm for expensive constrained multi-fidelity blackbox optimization

This work introduces a novel blackbox optimization algorithm for computationally expensive constrained multi-fidelity problems. When applying a direct search method to such problems, the scarcity of feasible points may lead to numerous costly evaluations spent on infeasible points. Our proposed fidelity and interruption controlled optimization algorithm addresses this issue by leveraging multi-fidelity information, allowing for … Read more

Sensitivity-based decision support for critical measures using the example of COVID-19 dynamics

We parametrize public policies in the context of the COVID-19 pandemic to evaluate the effectiveness of policies through sensitivity-based methods in order to offer insights into understanding the contributions to critical measures in retrospective. The study utilizes a group-specific SEIR model with a tracing and isolation strategy and vaccination programs. Public policies are applied to … Read more