26 papers · ranked by Valyu relevance
Zhen Yang, Lan Wu, Gen Li, Yichao Xu + 2 more
Integrated optimization of spatiotemporal resources at the intersection (IOSTRI) is crucial for traffic signal control, where both the lane allocation and signal control plans are optimized in a unified framework. This paper addresses the IOSTRI problem with delay minimization, formulating it as a binary mixed-integer…
Weifei Gan, Xin Zhou, Wangyu Wu, Chang-An Xu + 1 more
Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Zhiqian Zhai, Changhu Wang, Chengfeng Jiang, Ziqi Rong + 1 more
Integrating single-cell and spatial transcriptomics data across batches is essential for recovering comparable cell identities—including cell types, subtypes, and states—as a prerequisite for downstream analyses in multi-condition and large-scale studies. This task remains challenging because between-batch variation…
Julien Bückmann, Jorn van Kampen, Theo Hofman
This paper presents an approach and application of optimization of spatial packaging of interconnected systems with physical interactions (SPI2) in three-dimensional component placement problems. To enable its application for an automotive use case, SPI2 must support both initial design generation, including component…
Maximilian Siska, Emma Pajak, Katrin Rosenthal, Antonio del Rio Chanona + 2 more
Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Zijun Li, Aswin Kannan
We consider joint optimization and learning problems arising in real-time decision systems. While most existing work focuses primarily on convex, revenue-based objectives, we extend this line of research to multi-objective formulations. In energy systems, for instance, we incorporate metrics such as renewable…
Rimnoma S. Ouedraogo, Peter McCloskey, Beatrice Mwaipopo, Bipana Paudel Timilsena + 4 more
Banana and plantain (Musa spp.) production in Sub-Saharan Africa is severely constrained by multiple diseases, with Banana bunchy top virus (BBTV) representing the most devastating viral threat. Inadequate diagnostic infrastructure limits effective management, particularly for asymptomatic infections disseminated…
Michal Vasina, David Kovar, Martin Kizovsky, David Lacko + 19 more
Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for…
Carlos Lopera, Madelem Giraldo, Natalia Herrera
Microalgae and cyanobacteria have emerged as promising resources for sustainable agriculture; however, integrated methodologies for the rational design of crop-specific agricultural formulations remain scarce. This study proposes an integrated framework that combines biomass production, species characterization…
Hongli Wang, Zhiwei Chen, Xiaotong Fang, Antonio Javier Nakhal Akel
The aviation industry extensively employs integrated modular avionics (IMA) to enhance system efficiency by sharing resources across various functions. Despite the benefits, the design of IMA systems is not without its challenges, particularly in achieving cost-effectiveness, ensuring availability, and addressing…
Authors not listed
Designing efficient photoreactors remains challenging due to the complex interplay of light transport phenomena, shaped by reflection, scattering and absorption processes. Here, we introduce a workflow that integrates ray-tracing digital twins with multi-objective Bayesian optimization to autonomously design…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Serena Landers, Sahil Pontula, Shiekh Zia Uddin, Sachin Vaidya + 2 more
We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this…
Xietao Wang Lin, Juan Ungredda, Max Butler, James Town + 3 more
Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with decoupled black-box constraints, in which subsets of the objective and…
Yuhang Li, Shiqi Chen, Tingyu Gong, Aydogan Ozcan
Optical computing holds promise for high-speed, energy-efficient information processing, with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations. A challenge, however, is the effective optimization and alignment of the diffractive layers, which is hindered by the…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Gabriel Hernández-Morales, Brenda Cansino-Loeza, Arturo Jiménez-Gutiérrez, Victor M. Zavala
Simulation-based optimization of complex systems over discrete decision spaces is a challenging computational problem. Specifically, discrete decision spaces lead to a combinatorial explosion of possible alternatives, making it computationally prohibitive to perform simulations for all possible combinations. In this…
Mourad Naidji, Alla Eddine Toubal Maamar, Mohamed Ilyas Rahal, Saad Mekhilef + 2 more
Optimal Power Flow (OPF) is a highly nonlinear and constrained optimization problem that seeks optimal operating conditions while ensuring secure and efficient power system operation. Although metaheuristic algorithms have demonstrated strong global search capability for OPF, their performance is often limited by…
Daniel J. Laky, Shammah Lilonfe, Shawn B. Martin, Katherine A. Klise + 3 more
Digital twins require high-quality data to achieve predictive capability, but time and resource limitations make efficient experiment design essential. Model-based design of experiments can address this challenge, especially when coupled with equation-oriented optimization and first-principles models. Pyomo.DoE is a…
Fardad Homafar, Jasmin Jelovica
Structural optimization problems often involve a large number of decision variables and highly non-convex feasible regions, making convergence to the true Pareto front extremely challenging. Even when convergence is achievable, it typically requires thousands of function evaluations, resulting in significant…
Giannis Poulopoulos, Hercules Avramopoulos, Cosimo Trono
Highlights What are the main findings?1. Optical sensing in industry and smart manufacturing is not a single platform choice. Distributed fiber sensing, FBG sensors, integrated photonic sensors, and nanophotonic/plasmonic devices address different combinations of spatial coverage, sensing volume, measurand type, and…
Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer + 2 more
Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the…